448 Publications
2024 | Journal Article | LibreCat-ID: 53073
Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388–14396. https://doi.org/10.1609/aaai.v38i13.29352
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| DOI
2024 | Journal Article | LibreCat-ID: 54911
Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190
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| DOI
2024 | Journal Article | LibreCat-ID: 54910
Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190
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| DOI
2024 | Conference Paper | LibreCat-ID: 55311
Kolpaczki, P., Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3520–3528). PMLR.
LibreCat
2024 | Journal Article | LibreCat-ID: 54907
Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190
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| DOI
2024 | Conference Paper | LibreCat-ID: 57645
Heid, S., Kornowicz, J., Hanselle, J. M., Hüllermeier, E., & Thommes, K. (2024). Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems. PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE, 21, 233.
LibreCat
2024 | Conference Paper | LibreCat-ID: 55631
Javanmardi, A., Aimiyekagbon, O. K., Bender, A., Kimotho, J. K., Sextro, W., & Hüllermeier, E. (2024). Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions. PHM Society European Conference, 8(1), Article 9. https://doi.org/10.36001/phme.2024.v8i1.4101
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| DOI
2023 | Conference Paper | LibreCat-ID: 51373
Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2023). Probabilistic Scoring Lists for Interpretable Machine Learning. 26th International Conference on Discovery Science , 14050, 189–203. https://doi.org/10.1007/978-3-031-45275-8_13
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| DOI
2023 | Conference Paper | LibreCat-ID: 52230
Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer, B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. NeurIPS 2023 - Advances in Neural Information Processing Systems, 36, 11515--11551.
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2023 | Book Chapter | LibreCat-ID: 54613
Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing – Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
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| DOI
2023 | Book Chapter | LibreCat-ID: 54909
Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2023). Probabilistic Scoring Lists for Interpretable Machine Learning. In Discovery Science. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45275-8_13
LibreCat
| DOI
2023 | Preprint | LibreCat-ID: 44512 | 
Uhlemeyer, S., Lienen, J., Hüllermeier, E., & Gottschalk, H. (2023). Detecting Novelties with Empty Classes. In arXiv:2305.00983.
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| arXiv
2023 | Conference Paper | LibreCat-ID: 31880 |
Nguyen, D. A., Levie, R., Lienen, J., Kutyniok, G., & Hüllermeier, E. (2023). Memorization-Dilation: Modeling Neural Collapse Under Noise. International Conference on Learning Representations, ICLR. International Conference on Learning Representations, ICLR, Kigali, Ruanda.
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2023 | Book Chapter | LibreCat-ID: 45884 |
Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
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2023 | Book Chapter | LibreCat-ID: 45886 |
Wehrheim, H., Hüllermeier, E., Becker, S., Becker, M., Richter, C., & Sharma, A. (2023). Composition Analysis in Unknown Contexts. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 105–123). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068510
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2023 | Preprint | LibreCat-ID: 45911 |
Lienen, J., & Hüllermeier, E. (2023). Mitigating Label Noise through Data Ambiguation. In arXiv:2305.13764.
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| arXiv
2023 | Journal Article | LibreCat-ID: 21600
Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement learning. SIAM Journal on Scientific Computing, 45(2), A579–A595. https://doi.org/10.1137/21M1412682
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| arXiv
2023 | Book Chapter | LibreCat-ID: 48776
Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2023). iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43418-1_26
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| DOI
2023 | Conference Paper | LibreCat-ID: 48775
Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 Proceedings. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. https://doi.org/10.14428/esann/2023.es2023-148
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| DOI
2023 | Book Chapter | LibreCat-ID: 48778
Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., & Huellermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In Communications in Computer and Information Science. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44064-9_11
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| DOI
2023 | Conference Paper | LibreCat-ID: 51209
Hanselle, J. M., Kornowicz, J., Heid, S., Thommes, K., & Hüllermeier, E. (2023). Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. In M. Leyer & J. Wichmann (Eds.), LWDA’23: Learning, Knowledge, Data, Analysis. .
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2022 | Conference Paper | LibreCat-ID: 32311
Sharma, A., Melnikov, V., Hüllermeier, E., & Wehrheim, H. (2022). Property-Driven Testing of Black-Box Functions. Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), 113–123.
LibreCat
2022 | Conference Paper | LibreCat-ID: 34542
Campagner, A., Lienen, J., Hüllermeier, E., & Ciucci, D. (2022). Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. Lecture Notes in Computer Science, 13633, 57–70.
LibreCat
2022 | Preprint | LibreCat-ID: 31546 |
Lienen, J., Demir, C., & Hüllermeier, E. (2022). Conformal Credal Self-Supervised Learning. In arXiv:2205.15239.
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2022 | Journal Article | LibreCat-ID: 33090
Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. https://doi.org/10.1007/s40194-022-01339-9
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| DOI
2022 | Report | LibreCat-ID: 36227
Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl, U., Braun, M., Pfeifer, A., Holst, C.-A., Schmidt, M., Schomaker, G., & Tornede, T. (2022). Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. https://doi.org/10.4119/unibi/2965622
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| DOI
2022 | Journal Article | LibreCat-ID: 48780
Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2022). Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz, 36(3–4), 211–224. https://doi.org/10.1007/s13218-022-00766-6
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| DOI
2021 | Journal Article | LibreCat-ID: 24143
Drees, J. P., Gupta, P., Hüllermeier, E., Jager, T., Konze, A., Priesterjahn, C., Ramaswamy, A., & Somorovsky, J. (2021). Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! 14th ACM Workshop on Artificial Intelligence and Security.
LibreCat
2021 | Journal Article | LibreCat-ID: 24148
Ramaswamy, A., & Hüllermeier, E. (2021). Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. IEEE Transactions on Artificial Intelligence (to Appear).
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2021 | Journal Article | LibreCat-ID: 21004
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276
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| DOI
2021 | Journal Article | LibreCat-ID: 21092
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
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2021 | Conference Paper | LibreCat-ID: 21570
Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.
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2021 | Journal Article | LibreCat-ID: 21636
Lienen, J., & Hüllermeier, E. (2021). Instance weighting through data imprecisiation. International Journal of Approximate Reasoning.
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2021 | Conference Paper | LibreCat-ID: 21637 |
Lienen, J., & Hüllermeier, E. (2021). From Label Smoothing to Label Relaxation. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI (Vol. 35, pp. 8583–8591). Online: AAAI Press.
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2021 | Conference Paper | LibreCat-ID: 23779
Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., & Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada.
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2021 | Conference Paper | LibreCat-ID: 22280
Lienen, J., Hüllermeier, E., Ewerth, R., & Nommensen, N. (2021). Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 14595–14604.
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2021 | Preprint | LibreCat-ID: 22509 |
Lienen, J., & Hüllermeier, E. (2021). Credal Self-Supervised Learning. ArXiv:2106.11853.
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2021 | Conference Paper | LibreCat-ID: 22913
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).
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2021 | Conference Paper | LibreCat-ID: 27381
Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180). Springer. https://doi.org/10.1007/978-3-030-88942-5
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| DOI
| arXiv
2021 | Conference Paper | LibreCat-ID: 21198
Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.
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2021 | Book Chapter | LibreCat-ID: 29292 |
Feldhans, R., Wilke, A., Heindorf, S., Shaker, M. H., Hammer, B., Ngonga Ngomo, A.-C., & Hüllermeier, E. (2021). Drift Detection in Text Data with Document Embeddings. In Intelligent Data Engineering and Automated Learning – IDEAL 2021. Springer International Publishing. https://doi.org/10.1007/978-3-030-91608-4_11
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2021 | Journal Article | LibreCat-ID: 24456 |
Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier, H., Esposito, E., Grimminger, A., Hammer, B., Haeb-Umbach, R., Horwath, I., Hüllermeier, E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A.-C., Schulte, C., Wachsmuth, H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions on Cognitive and Developmental Systems, 13(3), 717–728. https://doi.org/10.1109/tcds.2020.3044366
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2021 | Working Paper | LibreCat-ID: 45616
van Straaten, D., Melnikov, V., Hüllermeier, E., Mir Djawadi, B., & Fahr, R. (2021). Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes (Vol. 72).
LibreCat
2020 | Preprint | LibreCat-ID: 19603 |
Bode, H., Heid, S. H., Weber, D., Hüllermeier, E., & Wallscheid, O. (2020). Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. ArXiv:2005.04869.
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2020 | Conference Paper | LibreCat-ID: 19953 |
Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.
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| arXiv
2020 | Preprint | LibreCat-ID: 20211 |
Lienen, J., & Hüllermeier, E. (2020). Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce model. ArXiv:2010.13118.
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2020 | Conference Paper | LibreCat-ID: 24146
Heid, S. H., Ramaswamy, A., & Hüllermeier, E. (2020). Constrained Multi-Agent Optimization with Unbounded Information Delay. Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020, 26, 247.
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2020 | Conference Paper | LibreCat-ID: 17407
Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020.
LibreCat
2020 | Conference Paper | LibreCat-ID: 17408
Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.
LibreCat
2020 | Conference Paper | LibreCat-ID: 17424
Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8
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| DOI
2020 | Preprint | LibreCat-ID: 17605 |
Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.
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2020 | Conference Paper | LibreCat-ID: 20306
Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.
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2020 | Book Chapter | LibreCat-ID: 18014
El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., & Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096, pp. 216–232). Cham: Springer. https://doi.org/10.1007/978-3-030-53552-0_22
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| DOI
2020 | Preprint | LibreCat-ID: 18017
El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce Model. ArXiv:2002.04275.
LibreCat
2020 | Conference Paper | LibreCat-ID: 18276
Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.
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2020 | Journal Article | LibreCat-ID: 16725
Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.
LibreCat
2020 | Conference Paper | LibreCat-ID: 15629
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.
LibreCat
2019 | Conference Abstract | LibreCat-ID: 8868
Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.
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2019 | Journal Article | LibreCat-ID: 10578
Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation, 15(2), 191–213.
LibreCat
2019 | Journal Article | LibreCat-ID: 15001
Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642
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| DOI
2019 | Journal Article | LibreCat-ID: 15002 |
Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5
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2019 | Conference Paper | LibreCat-ID: 15003
Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019.
LibreCat
2019 | Book Chapter | LibreCat-ID: 15004
Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_22
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| DOI
2019 | Book Chapter | LibreCat-ID: 15005
Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In KI 2019: Advances in Artificial Intelligence. Cham. https://doi.org/10.1007/978-3-030-30179-8_3
LibreCat
| DOI
2019 | Book Chapter | LibreCat-ID: 15006
Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_7
LibreCat
| DOI
2019 | Conference Paper | LibreCat-ID: 15007 |
Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101). https://doi.org/10.1016/j.jmva.2019.02.017
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2019 | Conference Paper | LibreCat-ID: 15011 |
Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.
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2019 | Conference Paper | LibreCat-ID: 15013
Brinker, K., & Hüllermeier, E. (2019). A Reduction of Label Ranking to Multiclass Classification. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany.
LibreCat
2019 | Conference Paper | LibreCat-ID: 15014
Hüllermeier, E., Couso, I., & Diestercke, S. (2019). Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management.
LibreCat
2019 | Journal Article | LibreCat-ID: 15015
Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions on Knowledge Discovery from Data, 1–36. https://doi.org/10.1145/3363572
LibreCat
| DOI
2019 | Conference Abstract | LibreCat-ID: 13132
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.
LibreCat
2019 | Conference Paper | LibreCat-ID: 10232 |
Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.
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2019 | Journal Article | LibreCat-ID: 20243
Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2019.2892991
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| DOI
2018 | Conference Paper | LibreCat-ID: 2479 |
Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039
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2018 | Conference Paper | LibreCat-ID: 2857 |
Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.
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2018 | Conference Paper | LibreCat-ID: 2471 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SCC.2018.00036
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2018 | Journal Article | LibreCat-ID: 3402
Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1
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2018 | Journal Article | LibreCat-ID: 3510 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z
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2018 | Conference Paper | LibreCat-ID: 3552 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19
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2018 | Conference Paper | LibreCat-ID: 3852 |
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
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2018 | Conference Paper | LibreCat-ID: 2109 |
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM. https://doi.org/10.1145/3205455.3205562
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2018 | Preprint | LibreCat-ID: 17713 |
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv.
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2018 | Preprint | LibreCat-ID: 17714 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine learning service composition.
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2018 | Book Chapter | LibreCat-ID: 6423
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01771-2_11
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2018 | Conference (Editor) | LibreCat-ID: 10591
Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C., … Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management (Vol. 7, pp. 1–29).
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2018 | Book Chapter | LibreCat-ID: 10783
Couso, I., & Hüllermeier, E. (2018). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim, A. Nürnberger, & C. Borgelt (Eds.), Frontiers in Computational Intelligence (pp. 31–46). Springer.
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2018 | Journal Article | LibreCat-ID: 16038
Schäfer, D., & Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.
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2018 | Conference Paper | LibreCat-ID: 10145
Ahmadi Fahandar, M., & Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958).
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2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul, A., Hüllermeier, E., & Busa-Fekete, R. (2018). Ranking Distributions based on Noisy Sorting. Proc. 35th Int. Conference on Machine Learning (ICML), 3469–3477.
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2018 | Conference Paper | LibreCat-ID: 10149
Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 15–20.
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2018 | Book Chapter | LibreCat-ID: 10152
Mencia, E. L., Fürnkranz, J., Hüllermeier, E., & Rapp, M. (2018). Learning interpretable rules for multi-label classification. In H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, & M. A. J. van Gerven (Eds.), Explainable and Interpretable Models in Computer Vision and Machine Learning (pp. 81–113). Springer.
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2018 | Conference Paper | LibreCat-ID: 10181
Nguyen, V.-L., Destercke, S., Masson, M.-H., & Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–5095.
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2018 | Conference Paper | LibreCat-ID: 10184
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. Proc. 21st Int. Conference on Discovery Science (DS), 161–175.
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2018 | Journal Article | LibreCat-ID: 10276
Schäfer, D., & Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.
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2018 | Conference Abstract | LibreCat-ID: 1379 |
Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., & Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart & D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft.
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2018 | Journal Article | LibreCat-ID: 22996
Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Procedia Manufacturing, 24, 15–20.
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2017 | Conference Paper | LibreCat-ID: 3325
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000074341
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2017 | Conference Paper | LibreCat-ID: 71
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262
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2017 | Report | LibreCat-ID: 72
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions.
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2017 | Encyclopedia Article | LibreCat-ID: 10589
Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005).
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2017 | Book Chapter | LibreCat-ID: 10784
Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (Vol. 107, pp. 1000–1005). Springer.
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2017 | Conference Paper | LibreCat-ID: 1180 |
Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.
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2017 | Conference Paper | LibreCat-ID: 15397
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the structure of nested dichotomies. A comparison of two heuristics. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany (pp. 1–12). KIT Scientific Publishing.
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2017 | Conference Paper | LibreCat-ID: 15399
Czech, M., Hüllermeier, E., Jacobs, M. C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany.
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2017 | Conference Paper | LibreCat-ID: 15110
Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum likelihood estimation and coarse data. In in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain (pp. 3–16). Springer.
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2017 | Conference Paper | LibreCat-ID: 10204
Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T., … Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924).
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2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar, M., Hüllermeier, E., & Couso, I. (2017). Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).
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2017 | Conference Paper | LibreCat-ID: 10206 |
Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206). https://doi.org/10.1007/978-3-319-67190-1_15
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2017 | Conference Paper | LibreCat-ID: 10207
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26).
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2017 | Conference Paper | LibreCat-ID: 10208
Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum Likelihood Estimation and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) (pp. 3–16).
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2017 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar, M., & Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence.
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2017 | Conference Paper | LibreCat-ID: 10212
Hoffmann, F., Hüllermeier, E., & Mikut, R. (2017). (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017.
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2017 | Conference Paper | LibreCat-ID: 10213
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 (pp. 1–12).
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2017 | Conference Paper | LibreCat-ID: 10216
Shaker, A., Heldt, W., & Hüllermeier, E. (2017). Learning TSK Fuzzy Rules from Data Streams. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia.
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2017 | Journal Article | LibreCat-ID: 10267
Bräuning, M., Hüllermeier, E., Keller, T., & Glaum, M. (2017). Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application in accounting. European Journal of Operational Research, 258(1), 295–306.
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2017 | Journal Article | LibreCat-ID: 10268
Platenius, M.-C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2017). Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Transactions on Software Engineering, 43(8), 739–759.
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2017 | Journal Article | LibreCat-ID: 10269
Hüllermeier, E. (2017). From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection. The Computing Research Repository (CoRR).
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2016 | Journal Article | LibreCat-ID: 3318
Melnikov, V., Hüllermeier, E., Kaimann, D., Frick, B., & Gupta, Pritha . (2016). Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae, 25. https://doi.org/10.4467/20838476si.16.006.6187
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2016 | Journal Article | LibreCat-ID: 190
Platenius, M. C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2016). Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, (8), 739–759. https://doi.org/10.1109/TSE.2016.2632115
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2016 | Conference Paper | LibreCat-ID: 184
Melnikov, V., & Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) (pp. 756–771). https://doi.org/10.1007/978-3-319-46227-1_47
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2016 | Encyclopedia Article | LibreCat-ID: 10785
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference Paper | LibreCat-ID: 15400
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the identifiability of models in multi-criteria preference learning. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Conference Paper | LibreCat-ID: 15401
Schäfer, D., & Hüllermeier, E. (2016). Preference -based reinforcement learning using dyad ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany.
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2016 | Conference Paper | LibreCat-ID: 15402
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Conference Paper | LibreCat-ID: 15403
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy superset losses. In E. Hüllermeier, F. Hoffmann, & R. Mikut (Eds.), in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany (pp. 1–8). KIT Scientific Publishing.
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2016 | Conference Paper | LibreCat-ID: 15404
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In in Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany.
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2016 | Conference Paper | LibreCat-ID: 15111
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
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2016 | Journal Article | LibreCat-ID: 16041
Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., & Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
LibreCat
2016 | Book Chapter | LibreCat-ID: 10214
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference (Editor) | LibreCat-ID: 10221
Hoffmann, F., Hüllermeier, E., & Mikut, R. (Eds.). (2016). Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.
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2016 | Conference Paper | LibreCat-ID: 10222
Jasinska, K., Dembczynski, K., Busa-Fekete, R., Klerx, T., & Hüllermeier, E. (2016). Extreme F-measure maximization using sparse probability estimates . In M. F. Balcan & K. Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA.
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2016 | Conference Paper | LibreCat-ID: 10223
Melnikov, V., & Hüllermeier, E. (2016). Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016. In European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 756–771).
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2016 | Conference Paper | LibreCat-ID: 10224
Dembczynski, K., Kotlowski, W., Waegeman, W., Busa-Fekete, R., & Hüllermeier, E. (2016). Consistency of probalistic classifier trees. In In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 511–526).
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2016 | Conference Paper | LibreCat-ID: 10225
Shabani, A., Paul, A., Platon, R., & Hüllermeier, E. (2016). Predicting the electricity consumption of buildings: An improved CBR approach. In In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA (pp. 356–369).
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2016 | Conference Paper | LibreCat-ID: 10226
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
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2016 | Conference Paper | LibreCat-ID: 10227
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the Identifiability of models in multi-criteria preference learning . In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10228
Schäfer, D., & Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10229
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10230
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy supersets losses. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing (pp. 1–8).
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2016 | Conference Paper | LibreCat-ID: 10231
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In In Workshop LWDA “Lernen, Wissen, Daten, Analysen.”
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2016 | Conference (Editor) | LibreCat-ID: 10263
Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F., & van Harmelen, F. (Eds.). (2016). ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence (Vol. 285). The Hague, The Netherlands: IOS Press.
LibreCat
2016 | Journal Article | LibreCat-ID: 10264
Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., & Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
LibreCat
2016 | Journal Article | LibreCat-ID: 10266
Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., & Heider, D. (2016). Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining, 9(10).
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2015 | Journal Article | LibreCat-ID: 4792
Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033. https://doi.org/10.1109/tfuzz.2015.2396078
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2015 | Conference Paper | LibreCat-ID: 15406
Schäfer, D., & Hüllermeier, E. (2015). Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal (pp. 110–111).
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2015 | Conference Paper | LibreCat-ID: 15749
Paul, A., & Hüllermeier, E. (2015). A cbr approach to the angry birds game. In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany (pp. 68–77).
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2015 | Conference Paper | LibreCat-ID: 15750
Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., & Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In F. Hoffmann & E. Hüllermeier (Eds.), In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany (pp. 235–240). KIT Scientific Publishing.
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2015 | Conference Paper | LibreCat-ID: 15751
Lu, S., & Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany (pp. 97–104). KIT Scientific Publishing.
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2015 | Journal Article | LibreCat-ID: 16049
Senge, R., & Hüllermeier, E. (2015). Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
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2015 | Journal Article | LibreCat-ID: 16051
Hüllermeier, E. (2015). From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.
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2015 | Journal Article | LibreCat-ID: 16053
Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
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2015 | Journal Article | LibreCat-ID: 16058
Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., & Hüllermeier, E. (2015). On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research, 15, 3313–3368.
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2015 | Journal Article | LibreCat-ID: 16067
Shaker, A., & Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
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2015 | Conference Paper | LibreCat-ID: 10234
Hüllermeier, E., & Minor, M. (2015). Case-Based Reasoning Research and Development . In in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer.
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2015 | Conference Paper | LibreCat-ID: 10235
Hoffmann, F., & Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.
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2015 | Conference Paper | LibreCat-ID: 10236
Abdel-Aziz, A., & Hüllermeier, E. (2015). Case Base Maintenance in Preference-Based CBR. In In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 1–14).
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2015 | Conference Paper | LibreCat-ID: 10237
Szörényi, B., Busa-Fekete, R., Weng, P., & Hüllermeier, E. (2015). Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In In Proceedings International Conference on Machine Learning (ICML 2015) (pp. 1660–1668).
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2015 | Conference Paper | LibreCat-ID: 10238
Schäfer, D., & Hüllermeier, E. (2015). Dyad Ranking Using A Bilinear Plackett-Luce Model. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 227–242).
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2015 | Conference Paper | LibreCat-ID: 10239
Hüllermeier, E., & Cheng, W. (2015). Superset Learning Based on Generalized Loss Minimization . In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 260–275).
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2015 | Conference Paper | LibreCat-ID: 10240
Henzgen, S., & Hüllermeier, E. (2015). Weighted Rank Correlation : A Flexible Approach Based on Fuzzy Order Relations. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 422–437).
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2015 | Conference Paper | LibreCat-ID: 10241
Szörényi, B., Busa-Fekete, R., Paul, A., & Hüllermeier, E. (2015). Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 604–612).
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2015 | Conference Paper | LibreCat-ID: 10242
Szörényi, B., Busa-Fekete, R., Dembczynski, K., & Hüllermeier, E. (2015). Online F-Measure Optimization. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 595–603).
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2015 | Conference Paper | LibreCat-ID: 10243
El Mesaoudi-Paul, A., & Hüllermeier, E. (2015). A CBR Approach to the Angry Birds Game. In in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 68–77).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10244
Schäfer, D., & Hüllermeier, E. (2015). Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations. In in Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML) (pp. 110–111).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10245
Lu, S., & Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In Proceedings 25. Workshop Computational Intelligence (pp. 97–104).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10246
Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., & Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In Proceedings 25. Workshop Computational Intelligence (pp. 235–240).
LibreCat
2015 | Journal Article | LibreCat-ID: 10319
Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., & Hüllermeier, E. (2015). On the Bayes-Optimality of F-Measure Maximizers. In Journal of Machine Learning Research, 15, 3333–3388.
LibreCat
2015 | Journal Article | LibreCat-ID: 10320
Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
LibreCat
2015 | Journal Article | LibreCat-ID: 10321
Shaker, A., & Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
LibreCat
2015 | Journal Article | LibreCat-ID: 10322
Hüllermeier, E. (2015). From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.
LibreCat
2015 | Journal Article | LibreCat-ID: 10323
Garcia-Jimenez, S., Bustince, U., Hüllermeier, E., Mesiar, R., Pal, N. R., & Pradera, A. (2015). Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 23(4), 1259–1273.
LibreCat
2015 | Journal Article | LibreCat-ID: 10324
Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
LibreCat
2014 | Journal Article | LibreCat-ID: 16046
Agarwal, M., Fallah Tehrani, A., & Hüllermeier, E. (2014). Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis, 22(3–4).
LibreCat
2014 | Journal Article | LibreCat-ID: 16060
Krotzky, T., Fober, T., Hüllermeier, E., & Klebe, G. (2014). Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 11(5), 878–890.
LibreCat
2014 | Journal Article | LibreCat-ID: 16064
Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning, 55(7), 1519–1534.
LibreCat
2014 | Journal Article | LibreCat-ID: 16069
Henzgen, S., Strickert, M., & Hüllermeier, E. (2014). Visualization of evolving fuzzy-rule-based systems. Evolving Systems, 5, 175–191.
LibreCat
2014 | Journal Article | LibreCat-ID: 16077
Busa-Fekete, R., Szörenyi, B., Weng, P., Cheng, W., & Hüllermeier, E. (2014). Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.
LibreCat
2014 | Journal Article | LibreCat-ID: 16078
Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V., … Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.
LibreCat
2014 | Journal Article | LibreCat-ID: 16079
Strickert, M., Bunte, K., Schleif, F. M., & Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.
LibreCat
2014 | Journal Article | LibreCat-ID: 16080
Shaker, A., & Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science, 24(1), 199–212.
LibreCat
2014 | Journal Article | LibreCat-ID: 16082
Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., & Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
LibreCat
2014 | Journal Article | LibreCat-ID: 16083
Donner-Banzhoff, N., Haasenritter, J., Hüllermeier, E., Viniol, A., Bösner, S., & Becker, A. (2014). The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology, 2(67), 124–132.
LibreCat
2014 | Conference Paper | LibreCat-ID: 10247
Busa-Fekete, R., Szörényi, B., & Hüllermeier, E. (2014). PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In Proceedings AAAI 2014, Quebec, Canada (pp. 1701–1707).
LibreCat
2014 | Conference Paper | LibreCat-ID: 10248
Busa-Fekete, R., & Hüllermeier, E. (2014). A Survey of Preference-Based Online Learning with Bandit Algorithms. In Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia (pp. 18–39).
LibreCat
2014 | Conference Paper | LibreCat-ID: 10249
Henzgen, S., & Hüllermeier, E. (2014). Mining Rank Data. In Proceedings Discovery Science, Bled,Slovenia (pp. 123–134).
LibreCat
2014 | Conference Paper | LibreCat-ID: 10250
Fallah Tehrani, A., Strickert, M., & Hüllermeier, E. (2014). The Choquet kernel for monotone data. In Proceedings ESANN , Bruges, Belgium.
LibreCat
2014 | Conference Paper | LibreCat-ID: 10251
Abdel-Aziz, A., Strickert, M., & Hüllermeier, E. (2014). Learning Solution Similarity in Preference-Based CBR. In Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland (pp. 17–31).
LibreCat
2014 | Conference Paper | LibreCat-ID: 10253
Schäfer, D., & Hüllermeier, E. (2014). Dyad Ranking Using A Bilinear Plackett-Luce Model. In Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany (pp. 32–33).
LibreCat
2014 | Conference Paper | LibreCat-ID: 10254
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In Proceedings, Parts I-III. Lecture Notes in Computer Science (pp. 8724–8726). Springer.
LibreCat
2014 | Conference Paper | LibreCat-ID: 10295
Fürnkranz, J., Hüllermeier, E., Rudin, C., Slowinski, R., & Sanner, S. (2014). Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports (Vol. 4, pp. 1–27).
LibreCat
2014 | Journal Article | LibreCat-ID: 10296
Shaker, A., & Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Applied Mathematics and Computer Science, 24(1), 199–212.
LibreCat
2014 | Journal Article | LibreCat-ID: 10297
Hoffmann, F., Hüllermeier, E., & Kroll, A. (2014). Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik, 62(10), 685–686.
LibreCat
2014 | Journal Article | LibreCat-ID: 10298
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Data Min. Knowledge Discovery, 28(5–6), 1129–1133.
LibreCat
2014 | Journal Article | LibreCat-ID: 10299
Henzgen, S., Strickert, M., & Hüllermeier, E. (2014). Visualization of evolving fuzzy rule-based systems. Evolving Systems, 5(3), 175–191.
LibreCat
2014 | Journal Article | LibreCat-ID: 10308
Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1519–1534.
LibreCat
2014 | Journal Article | LibreCat-ID: 10309
Hüllermeier, E. (2014). Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1609–1613.
LibreCat
2014 | Journal Article | LibreCat-ID: 10310
Strickert, M., Bunte, K., Schleif, F.-M., & Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.
LibreCat
2014 | Journal Article | LibreCat-ID: 10311
Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., & Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
LibreCat
2014 | Journal Article | LibreCat-ID: 10312
Mernberger, M., Moog, M., Stork, S., Zauner, S., Maier, U. G., & Hüllermeier, E. (2014). Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J. Bioinformatics and Computational Biology, 12(1).
LibreCat
2014 | Journal Article | LibreCat-ID: 10313
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Machine Learning, 97(1–2), 1–3.
LibreCat
2014 | Journal Article | LibreCat-ID: 10314
Busa-Fekete, R., Szörényi, B., Weng, P., Cheng, W., & Hüllermeier, E. (2014). Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.
LibreCat
2014 | Journal Article | LibreCat-ID: 10315
Montanés, E., Senge, R., Barranquero, J., Quevedo, J. R., Del Coz, J. J., & Hüllermeier, E. (2014). Dependent binary relevance models for multi-label classification. Pattern Recognition, 47(3), 1494–1508.
LibreCat
2014 | Journal Article | LibreCat-ID: 10316
Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V., … Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.
LibreCat
2014 | Journal Article | LibreCat-ID: 10317
Krotzky, T., Fober, T., Hüllermeier, E., & Klebe, G. (2014). Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Trans. Comput. Biology Bioinform., 11(5), 878–890.
LibreCat
2014 | Journal Article | LibreCat-ID: 10318
Stock, M., Fober, T., Hüllermeier, E., Glinca, S., Klebe, G., Pahikkala, T., … Wageman, W. (2014). Identification of Functionally Releated Enzymes by Learning to Rank Methods. IEEE/ACM Trans. Comput. Biology Bioinform., 11(6), 1157–1169.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15752
Cheng, W., Henzgen, S., & Hüllermeier, E. (2013). Labelwise versus pairwise decomposition in label ranking. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany (pp. 129–136).
LibreCat
2013 | Conference Paper | LibreCat-ID: 15753
Senge, R., del Coz, J., & Hüllermeier, E. (2013). Rectifying classifier chains for multi-label classification, Bamberg, Germany. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany (pp. 151–158).
LibreCat
2013 | Conference Paper | LibreCat-ID: 15755
Busa-Fekete, R., Fober, T., & Hüllermeier, E. (2013). Preference-based evolutionary optimization using generalized racing algorithms. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany (pp. 237–246). KIT Scientific Publishing.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15756
Henzgen, S., & Hüllermeier, E. (2013). Weighted rank correlation measures based on fuzzy order relations. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany (pp. 227–236). KIT Scientific Publishing.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15757
Weng, P., Busa-Fekete, R., & Hüllermeier, E. (2013). Interactive Q-learning with ordinal rewards and unreliable tutor. In In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15758
Busa-Fekete, R., Szörenyi, B., Weng, P., & Hüllermeier, E. (2013). Preference-based evolutionary direct policy search. In In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15759
Cheng, W., & Hüllermeier, E. (2013). A nearest neigbor approach to label ranking based on generalized labelwise loss minimization. In In Proceedings M-PREF`13, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15760
Shaker, A., & Hüllermeier, E. (2013). Event history analysis on data streams: An application to earthquake occurence. In G. Krempl, I. Zliobaite, Y. Wang, & G. Forman (Eds.), In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic (pp. 38–41).
LibreCat
2013 | Conference Paper | LibreCat-ID: 15761
Senge, R., del Coz, J. J., & Hüllermeier, E. (2013). On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery. In L. Schmidt-Thieme & M. Spiliopoulou (Eds.), In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany . Springer.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15763
Fober, T., Klebe, G., & Hüllermeier, E. (2013). Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics, Algorithms from and for Nature and Life. In B. Lausen, D. Van den Poel, & A. Ultsch (Eds.), In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany (pp. 279–286). Springer.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15112
Fallah Tehrani, A., & Hüllermeier, E. (2013). Ordinal Choquistic regression . In J. Montero, G. Pasi, & D. Ciucci (Eds.), in Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy. Atlantis Press.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15113
Nasiri, N., Fober, T., Senge, R., & Hüllermeier, E. (2013). Fuzzy Pattern Trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of colour yield in poyester dyeing, Edmonton, Canada. In in Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada (pp. 715–721).
LibreCat
2013 | Journal Article | LibreCat-ID: 16044
Heider, D., Senge, R., Cheng, W., & Hüllermeier, E. (2013). Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction. Bioinformatics, 29(16), 1946–1952.
LibreCat
2013 | Journal Article | LibreCat-ID: 16081
Bösner, S., Bönisch, K., Haasenritter , J., Schlegel, P., Hüllermeier, E., & Donner-Banzhoff, N. (2013). Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. . BMC Family Practice, 14(1), 154–162.
LibreCat
2013 | Journal Article | LibreCat-ID: 16086
Haasenritter, J., Viniol, A., Becker, A., Bösner, S., Hüllermeier, E., Senge, R., & Donner-Banzhoff, N. (2013). Diagnose im Kontext - eine erweiterte Perspektive. Zeitschrift Für Evidenz, Fortbildung Und Qualität Im Gesundheitswesen (ZEFQ), 107, 585–591.
LibreCat
2013 | Journal Article | LibreCat-ID: 16123
Shaker, A., Senge, R., & Hüllermeier, E. (2013). Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences, 220, 34–45.
LibreCat
2013 | Conference Paper | LibreCat-ID: 13115
Szarvas, G., Busa-Fekete, R., & Hüllermeier, E. (2013). Learning to rank lexical substitutions. In In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA.
LibreCat
2013 | Conference Paper | LibreCat-ID: 13116
Dembczynski, K., Jachnik, A., Kotlowski, W., Waegeman, W., & Hüllermeier, E. (2013). Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization. In S. Dasgupta & D. McAllester (Eds.), in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA (pp. 1130–1138).
LibreCat
2013 | Conference Paper | LibreCat-ID: 13117
Busa-Fekete, R., Szoreny, B., Weng, P., Cheng, W., & Hüllermeier, E. (2013). Top-k selection based on adaptive sampling of noisy preferences. In S. Dasgupta & D. McAllester (Eds.), in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA (pp. 1094–1102).
LibreCat
2013 | Conference Paper | LibreCat-ID: 13118
Hüllermeier, E., & Cheng, W. (2013). Preference-based CBR: General ideas and basic principles. In F. Rossi (Ed.), in Proceedings IJCAI-13, 23rd international Joint Conference on Artificial Intelligence, Beijing, China (pp. 3012–3016). AAAI Press.
LibreCat
2013 | Conference Paper | LibreCat-ID: 13119
Henzgen, S., Strickert, M., & Hüllermeier, E. (2013). Rule chains for visualizing evolving fuzzy rule-based systems. In R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, & A. Zolnierek (Eds.), in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland (pp. 279–288). Springer.
LibreCat
2013 | Conference Paper | LibreCat-ID: 13190
Shaker, A., & Hüllermeier, E. (2013). Recovery analysis for adaptive learning from non-stationary data streams. In R. Burduk, K. Jackowski, M. Kurzynski, W. Wozniak, & A. Zolnierek (Eds.), in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland (pp. 289–298). Springer.
LibreCat
2012 | Conference Paper | LibreCat-ID: 15299
Leinweber, M., Baumgärtner, L., Mernberger, M., Fober, T., Hüllermeier, E., Klebe, G., & Freisleben, B. (2012). GPU-based cloud computing for comparing the structure of protein binding sites. In in Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione d`Italia, Italy.
LibreCat
2012 | Book Chapter | LibreCat-ID: 15396
Hüllermeier, E., & Fallah Tehrani, A. (2012). Efficient learning of classifiers based on the 2-additive Choquet integral. In C. Moewes & A. Nürnberger (Eds.), Computational Intelligence in Intelligent Data Analysis (pp. 17–30). Springer.
LibreCat
2012 | Conference Paper | LibreCat-ID: 15754
Bräuning, M., & Hüllermeier, E. (2012). Learning conditional lexicographic preference trees. In In Workshops on Preference Learning at ECAI, European Conference on Artiticial intelligence, Montpellier, France.
LibreCat
2012 | Conference Paper | LibreCat-ID: 15114
Hüllermeier, E., & Fallah Tehrani, A. (2012). On the VC dimension of the Choquet integral. In In Proceedings IPMU-2012 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Catania, Italy.
LibreCat
2012 | Journal Article | LibreCat-ID: 16084
Fallah Tehrani, A., Cheng, W., Dembczynski, K., & Hüllermeier, E. (2012). Learning monotone nonlinear models using the Choquet integral. Machine Learning, 89(1), 183–211.
LibreCat
2012 | Journal Article | LibreCat-ID: 16085
Bustince, H., Pagola, M., Mesiar, R., Hüllermeier, E., & Herrera, F. (2012). Grouping, overlap and generalized bientropic functions for fuzzy modeling of pairwise comparisons. IEEE Transactions on Fuzzy Systems, 20(3), 405–415.
LibreCat
2012 | Journal Article | LibreCat-ID: 16087
Fürnkranz, J., Hüllermeier, E., Cheng, W., & Park, S. H. (2012). Preference-based reinforcement learning: A formal framework and a policy iteration algorithm. Machine Learning, 89(1), 123–156.
LibreCat
2012 | Journal Article | LibreCat-ID: 16088
Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2012). On label dependence and loss minimization in multi-label classification. Machine Learning, 88(1–2), 5–45.
LibreCat
2012 | Journal Article | LibreCat-ID: 16089
Shaker, A., & Hüllermeier, E. (2012). IBL Streams: A system for instance-based classification and regression on data streams. Evolving Systems, 3(4), 235–249.
LibreCat
2012 | Journal Article | LibreCat-ID: 16090
Dolorez Ruiz, M., & Hüllermeier, E. (2012). A formal and empirical analysis of the fuzzy gamma rank correlation coefficient. Information Sciences, 206, 1–17.
LibreCat
2012 | Journal Article | LibreCat-ID: 16091
Fallah Tehrani, A., Cheng, W., & Hüllermeier, E. (2012). Preference learning using the Choquet integral: The case of multipartite ranking. IEEE Transactions on Fuzzy Systems, 20(6), 1102–1113.
LibreCat
2012 | Journal Article | LibreCat-ID: 16092
Fober, T., Mernberger, M., Klebe, G., & Hüllermeier, E. (2012). Fingerprint kernels for protein structure comparison. Molecular Informatics, 31(6–7), 443–452.
LibreCat
2012 | Journal Article | LibreCat-ID: 16093
Hüllermeier, E., Rifqi, M., Henzgen, S., & Senge, R. (2012). Comparing fuzzy partitions: A generalization of the Rand index and related measures. IEEE Transactions on Fuzzy Systems, 20(3), 546–556.
LibreCat
2012 | Journal Article | LibreCat-ID: 16094
Senge, R., Fober, T., Nasiri, N., & Hüllermeier, E. (2012). Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung. At-Atomatisierungstechnik, 60(10), 622–629.
LibreCat
2012 | Conference Paper | LibreCat-ID: 13120
Cheng, W., Hüllermeier, E., Waegeman, W., & Welker, V. (2012). Label ranking with partial abstention based on thresholded probalistic models. In In Proceedings NIPS-2012, 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA.
LibreCat
2012 | Conference Paper | LibreCat-ID: 13191
Cheng, W., & Hüllermeier, E. (2012). Probability estimation for mulit-class classification based on label ranking. In Proceedings ECML/PKDD-2012, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, UK.
LibreCat
2012 | Conference Paper | LibreCat-ID: 13192
Dembczynski, K., Kotlowski, W., & Hüllermeier, E. (2012). Consistent multilabel ranking through univariate loss minimization. In J. Langford & J. Pineau (Eds.), in Proceedings ICML-2012, International Conference on Machine Learning, Edinburgh, Scotland.
LibreCat
2012 | Conference Paper | LibreCat-ID: 13193
Dembczynski, K., Waegeman, W., & Hüllermeier, E. (2012). An analysis of chaining in multi-label classification. In In Proceedings ECAI-2012, 20th European Conference on Artificial Inteligence, Montpellier, France (pp. 294–299). IOS Press.
LibreCat
2012 | Book Chapter | LibreCat-ID: 10153
Hüllermeier, E. (2012). Fuzzy rules in data mining: From fuzzy associations to gradual dependencies. In E. Trillas, P. P. Bonissone, L. Magdalena, & J. Kacprzyk (Eds.), Combining Experimentation and Theory (Vol. 271, pp. 123–135). Springer.
LibreCat
2011 | Conference Paper | LibreCat-ID: 15762
Dembczynski, K., Waegeman, W., & Hüllermeier, E. (2011). Joint mode estimation in multi-label classification by chaining. In In Proceedings ECML Workshop on Collective inference and Learning on Structured Data, Athens, Greece.
LibreCat
2011 | Conference Paper | LibreCat-ID: 15764
Mernberger, M., Moog, D., Stork, S., Zauner, S., Maier, U., & Hüllermeier, E. (2011). Prediction of protein localization for specialized compartments using time series kernels. In In Proceedings GCB-2011 German Conference on Bioinformatics Munich, Germany.
LibreCat
2011 | Conference Paper | LibreCat-ID: 15115
Nasiri, M., Hüllermeier, E., Senge, R., & Lughofer, E. (2011). Comparing methods for knowledge-driven and data-driven fuzzy modeling: A case study in textile industry. In in Proceedings IFSA-2011 World Congress of the International Fuzzy Systems Association, Surabaya and Bali Island, Indonesia (p. RW-103-1-6).
LibreCat
2011 | Conference Paper | LibreCat-ID: 15116
Lughofer, E., & Hüllermeier, E. (2011). On-line redundancy deletion in evolving fuzzy regression models using a fuzzy inclusion measure. In S. Galichet, J. Montero, & G. Mauris (Eds.), in Proceedings EUSFLAT-2011 7th International Conference on the European Society for Fuzzy Logic and Technology, Aix-les-Bains, France (pp. 380–387).
LibreCat
2011 | Conference Paper | LibreCat-ID: 15117
Fallah Tehrani, A., Cheng, W., & Hüllermeier, E. (2011). Choquistic regression: Generalizing logistic regression using the Choquet integral. In in Proceedings EUSFLAT-2011 7th International Conference on the European Society for Fuzzy Logic and Technology, Aix-les-Bains, France (pp. 868–875).
LibreCat
2011 | Journal Article | LibreCat-ID: 16119
Hirsch, O., Bösner, S., Hüllermeier, E., Senge, R., Dembczynski, K., & Donner-Banzhoff, N. (2011). Multivariate modeling to identify patterns in clinical data: The example of chest pain. BMC Medical Research Methodology, 11(155).
LibreCat
2011 | Journal Article | LibreCat-ID: 16126
Domshlak, C., Hüllermeier, E., Kaci, S., & Prade, H. (2011). Preferences in AL: An overview. Artificial Intelligence, 175(7–8), 1037–1052.
LibreCat
2011 | Journal Article | LibreCat-ID: 16147
Hüllermeier, E. (2011). Fuzzy machine learning and data mining. WIREs Data Mining and Knowledge Discovery, 1(4), 269–283.
LibreCat
2011 | Journal Article | LibreCat-ID: 16148
Mernberger, M., Klebe, G., & Hüllermeier, E. (2011). SEGA: Semi-global graph alignment for structure-based protein comparison. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 8(5), 1330–1343.
LibreCat
2011 | Journal Article | LibreCat-ID: 16149
Fober, T., Glinca, S., Klebe, G., & Hüllermeier, E. (2011). Superposition and alignment of labeled point clouds. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6), 1653–1666.
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2011 | Journal Article | LibreCat-ID: 16150
Hüllermeier, E. (2011). Fuzzy sets in machine learning and data mining. Applied Soft Computing Journal, 1493–1505.
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2011 | Journal Article | LibreCat-ID: 16153
Senge, R., & Hüllermeier, E. (2011). Top-down induction of fuzzy pattern trees. IEEE Transactions on Fuzzy Systems, 19(2), 241–252.
LibreCat
2011 | Conference Paper | LibreCat-ID: 13194
Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2011). An exact algorithm for F-measure maximization. In In Proceedings NIPS-2011, 25th Annual Conference on Neural Information Processing Systems, Granada, Spain.
LibreCat
2011 | Conference Paper | LibreCat-ID: 13195
Cheng, W., Fuernkranz, J., Hüllermeier, E., & Park, S. H. (2011). Preference-based policy iteration: Leveraging preference learning for reinforcement learning. In Proceedings ECML/PKDD-2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece, .
LibreCat
2011 | Conference Paper | LibreCat-ID: 13196
Fürnkranz, J., & Hüllermeier, E. (2011). Learning from label preferences. In T. Elomaa, J. Hollmen, & H. Mannila (Eds.), in Proceedings DS-2011, 14th International Conference on Discovery Science, number 6926 in LNAI (pp. 2–17). Springer.
LibreCat
2011 | Conference Paper | LibreCat-ID: 13197
Fallah Tehrani, A., Cheng, W., Dembczynski, K., & Hüllermeier, E. (2011). Learning monotone nonlinear models using the Choquet integral. In In Proceedings ECML/PKDD-2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece,.
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2011 | Conference Paper | LibreCat-ID: 13198
Hüllermeier, E., & Schlegel, P. (2011). Preference-based CBR: First steps toward a methodological framework. In A. Ram & N. Wiratunga (Eds.), In Proceedings ICCBR-2011, 19th International Conference on Case-Based Reasoning, number 6880 in LNAI (pp. 77–91). Springer.
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2011 | Conference Paper | LibreCat-ID: 13588
Kotlowski, W., Dembczynski, K., & Hüllermeier, E. (2011). Bipartite ranking through minimization of univariate loss. In in Proceedings ICML-2011, 28th International Conference on Machine Learning, Washington, USA.
LibreCat
2010 | Book Chapter | LibreCat-ID: 15297
Hüllermeier, E., & Hühn, J. (2010). An Analysis of the FURIA algorithm for fuzzy rule induction. In J. Koronacki, Z. W. Ras, S. T. Wierzchon, & J. Kacprzyk (Eds.), Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S.Michalski (Vol. 262, pp. 321–344). Springer.
LibreCat
2010 | Book Chapter | LibreCat-ID: 15298
Fürnkranz, J., & Hüllermeier, E. (2010). Preference Learning: An Introduction. In J. Fürnkranz & E. Hüllermeier (Eds.), Preference Learning (pp. 1–18). Springer.
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2010 | Conference Paper | LibreCat-ID: 15765
Fober, T., Mernberger, M., Klebe, G., & Hüllermeier, E. (2010). Efficient similarity retrieval for protein binding sites based on histogram comparison. In In Proceedings GCB-2010 German Conference on Bioinformatics Braunschweig, Germany (pp. 51–60).
LibreCat
2010 | Conference Paper | LibreCat-ID: 15772
Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2010). On label dependence in multi-label classification. In In Proceedings MLD-2010, 2nd International Workshop on Learning from Multi-Label Data, Haifa, Israel.
LibreCat
2010 | Conference Paper | LibreCat-ID: 15118
Fober, T., & Hüllermeier, E. (2010). Similarity measures for protein structures based on fuzzy histogram comparison. In in Proceedings WCCI-2010, World Congress on Computational Intelligence, Barcelona, Spain.
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2010 | Conference Paper | LibreCat-ID: 15119
Senge, R., & Hüllermeier, E. (2010). Pattern trees for regression and fuzzy systems modeling. In in Proceedings WCCI-2010, World Congress on Computational Intelligence, Barcelona, Spain.
LibreCat
2010 | Journal Article | LibreCat-ID: 16121
Pfeffer, P., Fober, T., Hüllermeier, E., & Klebe, G. (2010). GARLig: A fully automated tool for subset selection of large fragment spaces via a self-adaptive genetic algorithm. Journal of Chemical Information and Modeling, 50(9), 1644–1659.
LibreCat
2010 | Journal Article | LibreCat-ID: 16151
Hüllermeier, E., & Vanderlooy, S. (2010). Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognition, 43(1), 128–142.
LibreCat
2010 | Journal Article | LibreCat-ID: 16152
Hüllermeier, E., & Fürnkranz, J. (2010). On predictive accuracy and risk minimization in pairwise label ranking. Journal of Computer and System Sciences, 76(1), 49–62.
LibreCat
2010 | Conference Paper | LibreCat-ID: 13589
Dembczynski, K., Cheng, W., & Hüllermeier, E. (2010). Bayes optimal multilabel classification via probalistic classifier chains. In J. Fürnkranz & T. Joachims (Eds.), in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel (pp. 279–286).
LibreCat
2010 | Conference Paper | LibreCat-ID: 13590
Cheng, W., Dembczynski, K., & Hüllermeier, E. (2010). Label ranking based on the Plackett-Luce model. In J. Fürnkranz & T. Joachims (Eds.), in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel (pp. 215–222).
LibreCat
2010 | Conference Paper | LibreCat-ID: 13591
Cheng, W., Dembczynski, K., & Hüllermeier, E. (2010). Graded multi-label classification: The ordinal case. In J. Fürnkranz & T. Joachims (Eds.), in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel.
LibreCat
2010 | Conference Paper | LibreCat-ID: 13593
Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2010). Regret analysis for performance metrics in multi-label classification: The case of Hamming and subset zero-one loss. In In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain.
LibreCat
2010 | Conference Paper | LibreCat-ID: 13594
Cheng, W., Rademaker, M., De Beats, B., & Hüllermeier, E. (2010). Predicting partial orders: Ranking with abstention. In In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain.
LibreCat
2010 | Conference Paper | LibreCat-ID: 13597
Hüllermeier, E. (2010). Uncertainty in clustering and classification . In in Proceedings SUM 2010, International Conference on Scalable Uncertainty Management (Vol. 6379, pp. 16–19). Springer.
LibreCat
2009 | Conference Paper | LibreCat-ID: 15773
Cheng, W., & Hüllermeier, E. (2009). A simple instance-based approach to multilabel classification using the Mallows model. In In Proceedings MLD-2009 1st International Workshop on Learning from Multi-Label Data, Bled, Slovenia (pp. 28–38).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15774
Senge, R., & Hüllermeier, E. (2009). Learning pattern tree classifiers using a co-evolutionary algorithm. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 19th Workshop Computational Intelligence, Dortmund Germany (pp. 22–33). KIT Scientific Publishing.
LibreCat
2009 | Conference Paper | LibreCat-ID: 15775
Fober, T., Mernberger, M., Moritz, R., & Hüllermeier, E. (2009). Graph-kernels for the comparative analysis of protein active sites. In I. Grosse, S. Neumann, S. Posch, F. Schreiber, & P. . Stadler (Eds.), In Proceedings GCB-2009 German Conference on Bioinformatics Halle (Saale), Germany (pp. 21–31).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15776
Senge, R., & Hüllermeier, E. (2009). Learning pattern tree classifiers using a co-evolutionary algorithm. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Darmstadt, Germany (pp. 105–110).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15778
Fober, T., Mernberger, M., Melnikov, V., Moritz, R., & Hüllermeier, E. (2009). Extension and empirical comparison of graph-kernels for the analysis of protein active sites. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Darmstadt, Germany (pp. 30–36).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15120
Fober, T., Klebe, G., & Hüllermeier, E. (2009). Efficient construction of multiple geometrical alignments for the comparison of protein binding sites. In in Proceedings ISDA-2009, 9th international Conference on Intelligent Systems Design and Applications, Pisa, Italy (pp. 1251–1256).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15194
Boukhris, I., Elouedi, Z., Fober, T., Mernberger, M., & Hüllermeier, E. (2009). Similarity analysis of protein binding sites: A generalization of the maximum common subgraph measure based on quasi-clique detection. In In Proceedings IDA-2009, 9th International Symposium on Intelligent Data Analysis, Pisa / Italy (pp. 1245–1250).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15195
Fober, T., & Hüllermeier, E. (2009). Fuzzy modeling of labeled point cloud superposition for the comparison of protein binding sites. In in Proceedings IFSA / EUSFLAT-2009 World Congress of the Fuzzy Systems Association, Lissabon, Portugal (pp. 1299–1304).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15196
Hüllermeier, E., & Rifqi, M. (2009). A fuzzy variant of the Rand index for comparing clustering structures. In in Proceedings IFSA / EUSFLAT-2009 World Congress of the Fuzzy Systems Association, Lissabon, Portugal (pp. 1294–1298).
LibreCat
2009 | Journal Article | LibreCat-ID: 16154
Weskamp, N., Hüllermeier, E., & Klebe, G. (2009). Merging chemical and biological space: Structural mapping of enzyme binding pocket space. Proteins, 76(2), 317–330.
LibreCat
2009 | Journal Article | LibreCat-ID: 16158
Hühn, J., & Hüllermeier, E. (2009). FURIA: An Algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery , 19, 293–319.
LibreCat
2009 | Journal Article | LibreCat-ID: 16159
Hüllermeier, E., & Vanderlooy, S. (2009). Why fuzzy decision trees are good rankers. IEEE Transactions on Fuzzy Systems, 17(6), 1233–1244.
LibreCat
2009 | Journal Article | LibreCat-ID: 16160
Cheng, W., & Hüllermeier, E. (2009). Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, 76(2–3), 211–225.
LibreCat
2009 | Journal Article | LibreCat-ID: 16161
Yi, Y., Fober, T., & Hüllermeier, E. (2009). Fuzzy operator trees for modeling rating functions. International Journal of Computational Intelligence and Applications, 8(4), 413–428.
LibreCat
2009 | Journal Article | LibreCat-ID: 16162
Fober, T., Mernberger, M., Klebe, G., & Hüllermeier, E. (2009). Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. Bioinformatics, 25(16), 2110–2117.
LibreCat
2009 | Journal Article | LibreCat-ID: 16163
Hühn, J., & Hüllermeier, E. (2009). FR3: A fuzzy rule learner for inducing reliable classifiers. IEEE Transactions on Fuzzy Systems, 17(1), 138–149.
LibreCat
2009 | Journal Article | LibreCat-ID: 16165
Hüllermeier, E., Vladimirskiy, I., Prados Suarez, B., & Stauch, E. (2009). Supporting case-based retrieval by similarity skylines: Basic concepts and extensions. Künstliche Intelligenz, 1(09), 24–29.
LibreCat
448 Publications
2024 | Journal Article | LibreCat-ID: 53073
Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388–14396. https://doi.org/10.1609/aaai.v38i13.29352
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| DOI
2024 | Journal Article | LibreCat-ID: 54911
Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190
LibreCat
| DOI
2024 | Journal Article | LibreCat-ID: 54910
Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190
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| DOI
2024 | Conference Paper | LibreCat-ID: 55311
Kolpaczki, P., Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3520–3528). PMLR.
LibreCat
2024 | Journal Article | LibreCat-ID: 54907
Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190
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| DOI
2024 | Conference Paper | LibreCat-ID: 57645
Heid, S., Kornowicz, J., Hanselle, J. M., Hüllermeier, E., & Thommes, K. (2024). Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems. PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE, 21, 233.
LibreCat
2024 | Conference Paper | LibreCat-ID: 55631
Javanmardi, A., Aimiyekagbon, O. K., Bender, A., Kimotho, J. K., Sextro, W., & Hüllermeier, E. (2024). Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions. PHM Society European Conference, 8(1), Article 9. https://doi.org/10.36001/phme.2024.v8i1.4101
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| DOI
2023 | Conference Paper | LibreCat-ID: 51373
Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2023). Probabilistic Scoring Lists for Interpretable Machine Learning. 26th International Conference on Discovery Science , 14050, 189–203. https://doi.org/10.1007/978-3-031-45275-8_13
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| DOI
2023 | Conference Paper | LibreCat-ID: 52230
Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer, B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. NeurIPS 2023 - Advances in Neural Information Processing Systems, 36, 11515--11551.
LibreCat
2023 | Book Chapter | LibreCat-ID: 54613
Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing – Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
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| DOI
2023 | Book Chapter | LibreCat-ID: 54909
Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2023). Probabilistic Scoring Lists for Interpretable Machine Learning. In Discovery Science. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45275-8_13
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| DOI
2023 | Preprint | LibreCat-ID: 44512 |
Uhlemeyer, S., Lienen, J., Hüllermeier, E., & Gottschalk, H. (2023). Detecting Novelties with Empty Classes. In arXiv:2305.00983.
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| arXiv
2023 | Conference Paper | LibreCat-ID: 31880 |
Nguyen, D. A., Levie, R., Lienen, J., Kutyniok, G., & Hüllermeier, E. (2023). Memorization-Dilation: Modeling Neural Collapse Under Noise. International Conference on Learning Representations, ICLR. International Conference on Learning Representations, ICLR, Kigali, Ruanda.
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2023 | Book Chapter | LibreCat-ID: 45884 |
Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
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| Files available
| DOI
2023 | Book Chapter | LibreCat-ID: 45886 |
Wehrheim, H., Hüllermeier, E., Becker, S., Becker, M., Richter, C., & Sharma, A. (2023). Composition Analysis in Unknown Contexts. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 105–123). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068510
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| Files available
| DOI
2023 | Preprint | LibreCat-ID: 45911 |
Lienen, J., & Hüllermeier, E. (2023). Mitigating Label Noise through Data Ambiguation. In arXiv:2305.13764.
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| arXiv
2023 | Journal Article | LibreCat-ID: 21600
Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement learning. SIAM Journal on Scientific Computing, 45(2), A579–A595. https://doi.org/10.1137/21M1412682
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| arXiv
2023 | Book Chapter | LibreCat-ID: 48776
Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2023). iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43418-1_26
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| DOI
2023 | Conference Paper | LibreCat-ID: 48775
Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 Proceedings. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. https://doi.org/10.14428/esann/2023.es2023-148
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| DOI
2023 | Book Chapter | LibreCat-ID: 48778
Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., & Huellermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In Communications in Computer and Information Science. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44064-9_11
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| DOI
2023 | Conference Paper | LibreCat-ID: 51209
Hanselle, J. M., Kornowicz, J., Heid, S., Thommes, K., & Hüllermeier, E. (2023). Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. In M. Leyer & J. Wichmann (Eds.), LWDA’23: Learning, Knowledge, Data, Analysis. .
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2022 | Conference Paper | LibreCat-ID: 32311
Sharma, A., Melnikov, V., Hüllermeier, E., & Wehrheim, H. (2022). Property-Driven Testing of Black-Box Functions. Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), 113–123.
LibreCat
2022 | Conference Paper | LibreCat-ID: 34542
Campagner, A., Lienen, J., Hüllermeier, E., & Ciucci, D. (2022). Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. Lecture Notes in Computer Science, 13633, 57–70.
LibreCat
2022 | Preprint | LibreCat-ID: 31546 |
Lienen, J., Demir, C., & Hüllermeier, E. (2022). Conformal Credal Self-Supervised Learning. In arXiv:2205.15239.
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2022 | Journal Article | LibreCat-ID: 33090
Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. https://doi.org/10.1007/s40194-022-01339-9
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| DOI
2022 | Report | LibreCat-ID: 36227
Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl, U., Braun, M., Pfeifer, A., Holst, C.-A., Schmidt, M., Schomaker, G., & Tornede, T. (2022). Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. https://doi.org/10.4119/unibi/2965622
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| DOI
2022 | Journal Article | LibreCat-ID: 48780
Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2022). Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz, 36(3–4), 211–224. https://doi.org/10.1007/s13218-022-00766-6
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| DOI
2021 | Journal Article | LibreCat-ID: 24143
Drees, J. P., Gupta, P., Hüllermeier, E., Jager, T., Konze, A., Priesterjahn, C., Ramaswamy, A., & Somorovsky, J. (2021). Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! 14th ACM Workshop on Artificial Intelligence and Security.
LibreCat
2021 | Journal Article | LibreCat-ID: 24148
Ramaswamy, A., & Hüllermeier, E. (2021). Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. IEEE Transactions on Artificial Intelligence (to Appear).
LibreCat
2021 | Journal Article | LibreCat-ID: 21004
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276
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| DOI
2021 | Journal Article | LibreCat-ID: 21092
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
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2021 | Conference Paper | LibreCat-ID: 21570
Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.
LibreCat
2021 | Journal Article | LibreCat-ID: 21636
Lienen, J., & Hüllermeier, E. (2021). Instance weighting through data imprecisiation. International Journal of Approximate Reasoning.
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2021 | Conference Paper | LibreCat-ID: 21637 |
Lienen, J., & Hüllermeier, E. (2021). From Label Smoothing to Label Relaxation. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI (Vol. 35, pp. 8583–8591). Online: AAAI Press.
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2021 | Conference Paper | LibreCat-ID: 23779
Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., & Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada.
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2021 | Conference Paper | LibreCat-ID: 22280
Lienen, J., Hüllermeier, E., Ewerth, R., & Nommensen, N. (2021). Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 14595–14604.
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2021 | Preprint | LibreCat-ID: 22509 |
Lienen, J., & Hüllermeier, E. (2021). Credal Self-Supervised Learning. ArXiv:2106.11853.
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2021 | Conference Paper | LibreCat-ID: 22913
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).
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2021 | Conference Paper | LibreCat-ID: 27381
Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180). Springer. https://doi.org/10.1007/978-3-030-88942-5
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| DOI
| arXiv
2021 | Conference Paper | LibreCat-ID: 21198
Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.
LibreCat
2021 | Book Chapter | LibreCat-ID: 29292 |
Feldhans, R., Wilke, A., Heindorf, S., Shaker, M. H., Hammer, B., Ngonga Ngomo, A.-C., & Hüllermeier, E. (2021). Drift Detection in Text Data with Document Embeddings. In Intelligent Data Engineering and Automated Learning – IDEAL 2021. Springer International Publishing. https://doi.org/10.1007/978-3-030-91608-4_11
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2021 | Journal Article | LibreCat-ID: 24456 |
Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier, H., Esposito, E., Grimminger, A., Hammer, B., Haeb-Umbach, R., Horwath, I., Hüllermeier, E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A.-C., Schulte, C., Wachsmuth, H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions on Cognitive and Developmental Systems, 13(3), 717–728. https://doi.org/10.1109/tcds.2020.3044366
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2021 | Working Paper | LibreCat-ID: 45616
van Straaten, D., Melnikov, V., Hüllermeier, E., Mir Djawadi, B., & Fahr, R. (2021). Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes (Vol. 72).
LibreCat
2020 | Preprint | LibreCat-ID: 19603 |
Bode, H., Heid, S. H., Weber, D., Hüllermeier, E., & Wallscheid, O. (2020). Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. ArXiv:2005.04869.
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2020 | Conference Paper | LibreCat-ID: 19953 |
Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.
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| arXiv
2020 | Preprint | LibreCat-ID: 20211 |
Lienen, J., & Hüllermeier, E. (2020). Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce model. ArXiv:2010.13118.
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2020 | Conference Paper | LibreCat-ID: 24146
Heid, S. H., Ramaswamy, A., & Hüllermeier, E. (2020). Constrained Multi-Agent Optimization with Unbounded Information Delay. Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020, 26, 247.
LibreCat
2020 | Conference Paper | LibreCat-ID: 17407
Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020.
LibreCat
2020 | Conference Paper | LibreCat-ID: 17408
Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.
LibreCat
2020 | Conference Paper | LibreCat-ID: 17424
Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8
LibreCat
| DOI
2020 | Preprint | LibreCat-ID: 17605 |
Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.
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2020 | Conference Paper | LibreCat-ID: 20306
Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.
LibreCat
2020 | Book Chapter | LibreCat-ID: 18014
El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., & Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096, pp. 216–232). Cham: Springer. https://doi.org/10.1007/978-3-030-53552-0_22
LibreCat
| DOI
2020 | Preprint | LibreCat-ID: 18017
El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce Model. ArXiv:2002.04275.
LibreCat
2020 | Conference Paper | LibreCat-ID: 18276
Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.
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2020 | Journal Article | LibreCat-ID: 16725
Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.
LibreCat
2020 | Conference Paper | LibreCat-ID: 15629
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.
LibreCat
2019 | Conference Abstract | LibreCat-ID: 8868
Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.
LibreCat
| Files available
2019 | Journal Article | LibreCat-ID: 10578
Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation, 15(2), 191–213.
LibreCat
2019 | Journal Article | LibreCat-ID: 15001
Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642
LibreCat
| DOI
2019 | Journal Article | LibreCat-ID: 15002 |
Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5
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| DOI
2019 | Conference Paper | LibreCat-ID: 15003
Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019.
LibreCat
2019 | Book Chapter | LibreCat-ID: 15004
Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_22
LibreCat
| DOI
2019 | Book Chapter | LibreCat-ID: 15005
Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In KI 2019: Advances in Artificial Intelligence. Cham. https://doi.org/10.1007/978-3-030-30179-8_3
LibreCat
| DOI
2019 | Book Chapter | LibreCat-ID: 15006
Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_7
LibreCat
| DOI
2019 | Conference Paper | LibreCat-ID: 15007 |
Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101). https://doi.org/10.1016/j.jmva.2019.02.017
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| DOI
2019 | Conference Paper | LibreCat-ID: 15011 |
Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.
LibreCat
| Files available
2019 | Conference Paper | LibreCat-ID: 15013
Brinker, K., & Hüllermeier, E. (2019). A Reduction of Label Ranking to Multiclass Classification. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany.
LibreCat
2019 | Conference Paper | LibreCat-ID: 15014
Hüllermeier, E., Couso, I., & Diestercke, S. (2019). Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management.
LibreCat
2019 | Journal Article | LibreCat-ID: 15015
Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions on Knowledge Discovery from Data, 1–36. https://doi.org/10.1145/3363572
LibreCat
| DOI
2019 | Conference Abstract | LibreCat-ID: 13132
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.
LibreCat
2019 | Conference Paper | LibreCat-ID: 10232 |
Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.
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| Files available
2019 | Journal Article | LibreCat-ID: 20243
Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2019.2892991
LibreCat
| DOI
2018 | Conference Paper | LibreCat-ID: 2479 |
Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039
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2018 | Conference Paper | LibreCat-ID: 2857 |
Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.
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2018 | Conference Paper | LibreCat-ID: 2471 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SCC.2018.00036
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2018 | Journal Article | LibreCat-ID: 3402
Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1
LibreCat
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2018 | Journal Article | LibreCat-ID: 3510 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z
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2018 | Conference Paper | LibreCat-ID: 3552 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19
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2018 | Conference Paper | LibreCat-ID: 3852 |
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
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2018 | Conference Paper | LibreCat-ID: 2109 |
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM. https://doi.org/10.1145/3205455.3205562
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2018 | Preprint | LibreCat-ID: 17713 |
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv.
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2018 | Preprint | LibreCat-ID: 17714 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine learning service composition.
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2018 | Book Chapter | LibreCat-ID: 6423
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01771-2_11
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2018 | Conference (Editor) | LibreCat-ID: 10591
Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C., … Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management (Vol. 7, pp. 1–29).
LibreCat
2018 | Book Chapter | LibreCat-ID: 10783
Couso, I., & Hüllermeier, E. (2018). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim, A. Nürnberger, & C. Borgelt (Eds.), Frontiers in Computational Intelligence (pp. 31–46). Springer.
LibreCat
2018 | Journal Article | LibreCat-ID: 16038
Schäfer, D., & Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.
LibreCat
2018 | Conference Paper | LibreCat-ID: 10145
Ahmadi Fahandar, M., & Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958).
LibreCat
2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul, A., Hüllermeier, E., & Busa-Fekete, R. (2018). Ranking Distributions based on Noisy Sorting. Proc. 35th Int. Conference on Machine Learning (ICML), 3469–3477.
LibreCat
2018 | Conference Paper | LibreCat-ID: 10149
Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 15–20.
LibreCat
2018 | Book Chapter | LibreCat-ID: 10152
Mencia, E. L., Fürnkranz, J., Hüllermeier, E., & Rapp, M. (2018). Learning interpretable rules for multi-label classification. In H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, & M. A. J. van Gerven (Eds.), Explainable and Interpretable Models in Computer Vision and Machine Learning (pp. 81–113). Springer.
LibreCat
2018 | Conference Paper | LibreCat-ID: 10181
Nguyen, V.-L., Destercke, S., Masson, M.-H., & Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–5095.
LibreCat
2018 | Conference Paper | LibreCat-ID: 10184
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. Proc. 21st Int. Conference on Discovery Science (DS), 161–175.
LibreCat
2018 | Journal Article | LibreCat-ID: 10276
Schäfer, D., & Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.
LibreCat
2018 | Conference Abstract | LibreCat-ID: 1379 |
Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., & Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart & D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft.
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2018 | Journal Article | LibreCat-ID: 22996
Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Procedia Manufacturing, 24, 15–20.
LibreCat
2017 | Conference Paper | LibreCat-ID: 3325
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000074341
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2017 | Conference Paper | LibreCat-ID: 71
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262
LibreCat
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| DOI
2017 | Report | LibreCat-ID: 72
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions.
LibreCat
| Files available
2017 | Encyclopedia Article | LibreCat-ID: 10589
Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005).
LibreCat
2017 | Book Chapter | LibreCat-ID: 10784
Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (Vol. 107, pp. 1000–1005). Springer.
LibreCat
2017 | Conference Paper | LibreCat-ID: 1180 |
Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.
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2017 | Conference Paper | LibreCat-ID: 15397
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the structure of nested dichotomies. A comparison of two heuristics. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany (pp. 1–12). KIT Scientific Publishing.
LibreCat
2017 | Conference Paper | LibreCat-ID: 15399
Czech, M., Hüllermeier, E., Jacobs, M. C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany.
LibreCat
2017 | Conference Paper | LibreCat-ID: 15110
Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum likelihood estimation and coarse data. In in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain (pp. 3–16). Springer.
LibreCat
2017 | Conference Paper | LibreCat-ID: 10204
Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T., … Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924).
LibreCat
2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar, M., Hüllermeier, E., & Couso, I. (2017). Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).
LibreCat
2017 | Conference Paper | LibreCat-ID: 10206 |
Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206). https://doi.org/10.1007/978-3-319-67190-1_15
LibreCat
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2017 | Conference Paper | LibreCat-ID: 10207
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26).
LibreCat
2017 | Conference Paper | LibreCat-ID: 10208
Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum Likelihood Estimation and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) (pp. 3–16).
LibreCat
2017 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar, M., & Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence.
LibreCat
2017 | Conference Paper | LibreCat-ID: 10212
Hoffmann, F., Hüllermeier, E., & Mikut, R. (2017). (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017.
LibreCat
2017 | Conference Paper | LibreCat-ID: 10213
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 (pp. 1–12).
LibreCat
2017 | Conference Paper | LibreCat-ID: 10216
Shaker, A., Heldt, W., & Hüllermeier, E. (2017). Learning TSK Fuzzy Rules from Data Streams. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia.
LibreCat
2017 | Journal Article | LibreCat-ID: 10267
Bräuning, M., Hüllermeier, E., Keller, T., & Glaum, M. (2017). Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application in accounting. European Journal of Operational Research, 258(1), 295–306.
LibreCat
2017 | Journal Article | LibreCat-ID: 10268
Platenius, M.-C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2017). Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Transactions on Software Engineering, 43(8), 739–759.
LibreCat
2017 | Journal Article | LibreCat-ID: 10269
Hüllermeier, E. (2017). From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection. The Computing Research Repository (CoRR).
LibreCat
2016 | Journal Article | LibreCat-ID: 3318
Melnikov, V., Hüllermeier, E., Kaimann, D., Frick, B., & Gupta, Pritha . (2016). Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae, 25. https://doi.org/10.4467/20838476si.16.006.6187
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2016 | Journal Article | LibreCat-ID: 190
Platenius, M. C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2016). Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, (8), 739–759. https://doi.org/10.1109/TSE.2016.2632115
LibreCat
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2016 | Conference Paper | LibreCat-ID: 184
Melnikov, V., & Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) (pp. 756–771). https://doi.org/10.1007/978-3-319-46227-1_47
LibreCat
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2016 | Encyclopedia Article | LibreCat-ID: 10785
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference Paper | LibreCat-ID: 15400
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the identifiability of models in multi-criteria preference learning. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
LibreCat
2016 | Conference Paper | LibreCat-ID: 15401
Schäfer, D., & Hüllermeier, E. (2016). Preference -based reinforcement learning using dyad ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany.
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2016 | Conference Paper | LibreCat-ID: 15402
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Conference Paper | LibreCat-ID: 15403
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy superset losses. In E. Hüllermeier, F. Hoffmann, & R. Mikut (Eds.), in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany (pp. 1–8). KIT Scientific Publishing.
LibreCat
2016 | Conference Paper | LibreCat-ID: 15404
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In in Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany.
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2016 | Conference Paper | LibreCat-ID: 15111
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
LibreCat
2016 | Journal Article | LibreCat-ID: 16041
Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., & Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
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2016 | Book Chapter | LibreCat-ID: 10214
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
LibreCat
2016 | Conference (Editor) | LibreCat-ID: 10221
Hoffmann, F., Hüllermeier, E., & Mikut, R. (Eds.). (2016). Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.
LibreCat
2016 | Conference Paper | LibreCat-ID: 10222
Jasinska, K., Dembczynski, K., Busa-Fekete, R., Klerx, T., & Hüllermeier, E. (2016). Extreme F-measure maximization using sparse probability estimates . In M. F. Balcan & K. Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA.
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2016 | Conference Paper | LibreCat-ID: 10223
Melnikov, V., & Hüllermeier, E. (2016). Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016. In European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 756–771).
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2016 | Conference Paper | LibreCat-ID: 10224
Dembczynski, K., Kotlowski, W., Waegeman, W., Busa-Fekete, R., & Hüllermeier, E. (2016). Consistency of probalistic classifier trees. In In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 511–526).
LibreCat
2016 | Conference Paper | LibreCat-ID: 10225
Shabani, A., Paul, A., Platon, R., & Hüllermeier, E. (2016). Predicting the electricity consumption of buildings: An improved CBR approach. In In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA (pp. 356–369).
LibreCat
2016 | Conference Paper | LibreCat-ID: 10226
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
LibreCat
2016 | Conference Paper | LibreCat-ID: 10227
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the Identifiability of models in multi-criteria preference learning . In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10228
Schäfer, D., & Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
LibreCat
2016 | Conference Paper | LibreCat-ID: 10229
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
LibreCat
2016 | Conference Paper | LibreCat-ID: 10230
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy supersets losses. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing (pp. 1–8).
LibreCat
2016 | Conference Paper | LibreCat-ID: 10231
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In In Workshop LWDA “Lernen, Wissen, Daten, Analysen.”
LibreCat
2016 | Conference (Editor) | LibreCat-ID: 10263
Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F., & van Harmelen, F. (Eds.). (2016). ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence (Vol. 285). The Hague, The Netherlands: IOS Press.
LibreCat
2016 | Journal Article | LibreCat-ID: 10264
Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., & Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
LibreCat
2016 | Journal Article | LibreCat-ID: 10266
Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., & Heider, D. (2016). Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining, 9(10).
LibreCat
2015 | Journal Article | LibreCat-ID: 4792
Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033. https://doi.org/10.1109/tfuzz.2015.2396078
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2015 | Conference Paper | LibreCat-ID: 15406
Schäfer, D., & Hüllermeier, E. (2015). Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal (pp. 110–111).
LibreCat
2015 | Conference Paper | LibreCat-ID: 15749
Paul, A., & Hüllermeier, E. (2015). A cbr approach to the angry birds game. In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany (pp. 68–77).
LibreCat
2015 | Conference Paper | LibreCat-ID: 15750
Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., & Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In F. Hoffmann & E. Hüllermeier (Eds.), In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany (pp. 235–240). KIT Scientific Publishing.
LibreCat
2015 | Conference Paper | LibreCat-ID: 15751
Lu, S., & Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany (pp. 97–104). KIT Scientific Publishing.
LibreCat
2015 | Journal Article | LibreCat-ID: 16049
Senge, R., & Hüllermeier, E. (2015). Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
LibreCat
2015 | Journal Article | LibreCat-ID: 16051
Hüllermeier, E. (2015). From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.
LibreCat
2015 | Journal Article | LibreCat-ID: 16053
Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
LibreCat
2015 | Journal Article | LibreCat-ID: 16058
Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., & Hüllermeier, E. (2015). On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research, 15, 3313–3368.
LibreCat
2015 | Journal Article | LibreCat-ID: 16067
Shaker, A., & Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
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2015 | Conference Paper | LibreCat-ID: 10234
Hüllermeier, E., & Minor, M. (2015). Case-Based Reasoning Research and Development . In in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer.
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2015 | Conference Paper | LibreCat-ID: 10235
Hoffmann, F., & Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.
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2015 | Conference Paper | LibreCat-ID: 10236
Abdel-Aziz, A., & Hüllermeier, E. (2015). Case Base Maintenance in Preference-Based CBR. In In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 1–14).
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2015 | Conference Paper | LibreCat-ID: 10237
Szörényi, B., Busa-Fekete, R., Weng, P., & Hüllermeier, E. (2015). Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In In Proceedings International Conference on Machine Learning (ICML 2015) (pp. 1660–1668).
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2015 | Conference Paper | LibreCat-ID: 10238
Schäfer, D., & Hüllermeier, E. (2015). Dyad Ranking Using A Bilinear Plackett-Luce Model. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 227–242).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10239
Hüllermeier, E., & Cheng, W. (2015). Superset Learning Based on Generalized Loss Minimization . In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 260–275).
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2015 | Conference Paper | LibreCat-ID: 10240
Henzgen, S., & Hüllermeier, E. (2015). Weighted Rank Correlation : A Flexible Approach Based on Fuzzy Order Relations. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 422–437).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10241
Szörényi, B., Busa-Fekete, R., Paul, A., & Hüllermeier, E. (2015). Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 604–612).
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2015 | Conference Paper | LibreCat-ID: 10242
Szörényi, B., Busa-Fekete, R., Dembczynski, K., & Hüllermeier, E. (2015). Online F-Measure Optimization. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 595–603).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10243
El Mesaoudi-Paul, A., & Hüllermeier, E. (2015). A CBR Approach to the Angry Birds Game. In in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 68–77).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10244
Schäfer, D., & Hüllermeier, E. (2015). Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations. In in Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML) (pp. 110–111).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10245
Lu, S., & Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In Proceedings 25. Workshop Computational Intelligence (pp. 97–104).
LibreCat
2015 | Conference Paper | LibreCat-ID: 10246
Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., & Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In Proceedings 25. Workshop Computational Intelligence (pp. 235–240).
LibreCat
2015 | Journal Article | LibreCat-ID: 10319
Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., & Hüllermeier, E. (2015). On the Bayes-Optimality of F-Measure Maximizers. In Journal of Machine Learning Research, 15, 3333–3388.
LibreCat
2015 | Journal Article | LibreCat-ID: 10320
Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
LibreCat
2015 | Journal Article | LibreCat-ID: 10321
Shaker, A., & Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
LibreCat
2015 | Journal Article | LibreCat-ID: 10322
Hüllermeier, E. (2015). From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.
LibreCat
2015 | Journal Article | LibreCat-ID: 10323
Garcia-Jimenez, S., Bustince, U., Hüllermeier, E., Mesiar, R., Pal, N. R., & Pradera, A. (2015). Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 23(4), 1259–1273.
LibreCat
2015 | Journal Article | LibreCat-ID: 10324
Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
LibreCat
2014 | Journal Article | LibreCat-ID: 16046
Agarwal, M., Fallah Tehrani, A., & Hüllermeier, E. (2014). Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis, 22(3–4).
LibreCat
2014 | Journal Article | LibreCat-ID: 16060
Krotzky, T., Fober, T., Hüllermeier, E., & Klebe, G. (2014). Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 11(5), 878–890.
LibreCat
2014 | Journal Article | LibreCat-ID: 16064
Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning, 55(7), 1519–1534.
LibreCat
2014 | Journal Article | LibreCat-ID: 16069
Henzgen, S., Strickert, M., & Hüllermeier, E. (2014). Visualization of evolving fuzzy-rule-based systems. Evolving Systems, 5, 175–191.
LibreCat
2014 | Journal Article | LibreCat-ID: 16077
Busa-Fekete, R., Szörenyi, B., Weng, P., Cheng, W., & Hüllermeier, E. (2014). Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.
LibreCat
2014 | Journal Article | LibreCat-ID: 16078
Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V., … Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.
LibreCat
2014 | Journal Article | LibreCat-ID: 16079
Strickert, M., Bunte, K., Schleif, F. M., & Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.
LibreCat
2014 | Journal Article | LibreCat-ID: 16080
Shaker, A., & Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science, 24(1), 199–212.
LibreCat
2014 | Journal Article | LibreCat-ID: 16082
Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., & Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
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2014 | Journal Article | LibreCat-ID: 16083
Donner-Banzhoff, N., Haasenritter, J., Hüllermeier, E., Viniol, A., Bösner, S., & Becker, A. (2014). The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology, 2(67), 124–132.
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2014 | Conference Paper | LibreCat-ID: 10247
Busa-Fekete, R., Szörényi, B., & Hüllermeier, E. (2014). PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In Proceedings AAAI 2014, Quebec, Canada (pp. 1701–1707).
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2014 | Conference Paper | LibreCat-ID: 10248
Busa-Fekete, R., & Hüllermeier, E. (2014). A Survey of Preference-Based Online Learning with Bandit Algorithms. In Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia (pp. 18–39).
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2014 | Conference Paper | LibreCat-ID: 10249
Henzgen, S., & Hüllermeier, E. (2014). Mining Rank Data. In Proceedings Discovery Science, Bled,Slovenia (pp. 123–134).
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2014 | Conference Paper | LibreCat-ID: 10250
Fallah Tehrani, A., Strickert, M., & Hüllermeier, E. (2014). The Choquet kernel for monotone data. In Proceedings ESANN , Bruges, Belgium.
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2014 | Conference Paper | LibreCat-ID: 10251
Abdel-Aziz, A., Strickert, M., & Hüllermeier, E. (2014). Learning Solution Similarity in Preference-Based CBR. In Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland (pp. 17–31).
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2014 | Conference Paper | LibreCat-ID: 10253
Schäfer, D., & Hüllermeier, E. (2014). Dyad Ranking Using A Bilinear Plackett-Luce Model. In Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany (pp. 32–33).
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2014 | Conference Paper | LibreCat-ID: 10254
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In Proceedings, Parts I-III. Lecture Notes in Computer Science (pp. 8724–8726). Springer.
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2014 | Conference Paper | LibreCat-ID: 10295
Fürnkranz, J., Hüllermeier, E., Rudin, C., Slowinski, R., & Sanner, S. (2014). Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports (Vol. 4, pp. 1–27).
LibreCat
2014 | Journal Article | LibreCat-ID: 10296
Shaker, A., & Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Applied Mathematics and Computer Science, 24(1), 199–212.
LibreCat
2014 | Journal Article | LibreCat-ID: 10297
Hoffmann, F., Hüllermeier, E., & Kroll, A. (2014). Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik, 62(10), 685–686.
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2014 | Journal Article | LibreCat-ID: 10298
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Data Min. Knowledge Discovery, 28(5–6), 1129–1133.
LibreCat
2014 | Journal Article | LibreCat-ID: 10299
Henzgen, S., Strickert, M., & Hüllermeier, E. (2014). Visualization of evolving fuzzy rule-based systems. Evolving Systems, 5(3), 175–191.
LibreCat
2014 | Journal Article | LibreCat-ID: 10308
Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1519–1534.
LibreCat
2014 | Journal Article | LibreCat-ID: 10309
Hüllermeier, E. (2014). Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1609–1613.
LibreCat
2014 | Journal Article | LibreCat-ID: 10310
Strickert, M., Bunte, K., Schleif, F.-M., & Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.
LibreCat
2014 | Journal Article | LibreCat-ID: 10311
Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., & Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
LibreCat
2014 | Journal Article | LibreCat-ID: 10312
Mernberger, M., Moog, M., Stork, S., Zauner, S., Maier, U. G., & Hüllermeier, E. (2014). Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J. Bioinformatics and Computational Biology, 12(1).
LibreCat
2014 | Journal Article | LibreCat-ID: 10313
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Machine Learning, 97(1–2), 1–3.
LibreCat
2014 | Journal Article | LibreCat-ID: 10314
Busa-Fekete, R., Szörényi, B., Weng, P., Cheng, W., & Hüllermeier, E. (2014). Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.
LibreCat
2014 | Journal Article | LibreCat-ID: 10315
Montanés, E., Senge, R., Barranquero, J., Quevedo, J. R., Del Coz, J. J., & Hüllermeier, E. (2014). Dependent binary relevance models for multi-label classification. Pattern Recognition, 47(3), 1494–1508.
LibreCat
2014 | Journal Article | LibreCat-ID: 10316
Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V., … Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.
LibreCat
2014 | Journal Article | LibreCat-ID: 10317
Krotzky, T., Fober, T., Hüllermeier, E., & Klebe, G. (2014). Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Trans. Comput. Biology Bioinform., 11(5), 878–890.
LibreCat
2014 | Journal Article | LibreCat-ID: 10318
Stock, M., Fober, T., Hüllermeier, E., Glinca, S., Klebe, G., Pahikkala, T., … Wageman, W. (2014). Identification of Functionally Releated Enzymes by Learning to Rank Methods. IEEE/ACM Trans. Comput. Biology Bioinform., 11(6), 1157–1169.
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2013 | Conference Paper | LibreCat-ID: 15752
Cheng, W., Henzgen, S., & Hüllermeier, E. (2013). Labelwise versus pairwise decomposition in label ranking. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany (pp. 129–136).
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2013 | Conference Paper | LibreCat-ID: 15753
Senge, R., del Coz, J., & Hüllermeier, E. (2013). Rectifying classifier chains for multi-label classification, Bamberg, Germany. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany (pp. 151–158).
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2013 | Conference Paper | LibreCat-ID: 15755
Busa-Fekete, R., Fober, T., & Hüllermeier, E. (2013). Preference-based evolutionary optimization using generalized racing algorithms. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany (pp. 237–246). KIT Scientific Publishing.
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2013 | Conference Paper | LibreCat-ID: 15756
Henzgen, S., & Hüllermeier, E. (2013). Weighted rank correlation measures based on fuzzy order relations. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany (pp. 227–236). KIT Scientific Publishing.
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2013 | Conference Paper | LibreCat-ID: 15757
Weng, P., Busa-Fekete, R., & Hüllermeier, E. (2013). Interactive Q-learning with ordinal rewards and unreliable tutor. In In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague.
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2013 | Conference Paper | LibreCat-ID: 15758
Busa-Fekete, R., Szörenyi, B., Weng, P., & Hüllermeier, E. (2013). Preference-based evolutionary direct policy search. In In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague.
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2013 | Conference Paper | LibreCat-ID: 15759
Cheng, W., & Hüllermeier, E. (2013). A nearest neigbor approach to label ranking based on generalized labelwise loss minimization. In In Proceedings M-PREF`13, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China.
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2013 | Conference Paper | LibreCat-ID: 15760
Shaker, A., & Hüllermeier, E. (2013). Event history analysis on data streams: An application to earthquake occurence. In G. Krempl, I. Zliobaite, Y. Wang, & G. Forman (Eds.), In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic (pp. 38–41).
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2013 | Conference Paper | LibreCat-ID: 15761
Senge, R., del Coz, J. J., & Hüllermeier, E. (2013). On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery. In L. Schmidt-Thieme & M. Spiliopoulou (Eds.), In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany . Springer.
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2013 | Conference Paper | LibreCat-ID: 15763
Fober, T., Klebe, G., & Hüllermeier, E. (2013). Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics, Algorithms from and for Nature and Life. In B. Lausen, D. Van den Poel, & A. Ultsch (Eds.), In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany (pp. 279–286). Springer.
LibreCat
2013 | Conference Paper | LibreCat-ID: 15112
Fallah Tehrani, A., & Hüllermeier, E. (2013). Ordinal Choquistic regression . In J. Montero, G. Pasi, & D. Ciucci (Eds.), in Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy. Atlantis Press.
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2013 | Conference Paper | LibreCat-ID: 15113
Nasiri, N., Fober, T., Senge, R., & Hüllermeier, E. (2013). Fuzzy Pattern Trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of colour yield in poyester dyeing, Edmonton, Canada. In in Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada (pp. 715–721).
LibreCat
2013 | Journal Article | LibreCat-ID: 16044
Heider, D., Senge, R., Cheng, W., & Hüllermeier, E. (2013). Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction. Bioinformatics, 29(16), 1946–1952.
LibreCat
2013 | Journal Article | LibreCat-ID: 16081
Bösner, S., Bönisch, K., Haasenritter , J., Schlegel, P., Hüllermeier, E., & Donner-Banzhoff, N. (2013). Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. . BMC Family Practice, 14(1), 154–162.
LibreCat
2013 | Journal Article | LibreCat-ID: 16086
Haasenritter, J., Viniol, A., Becker, A., Bösner, S., Hüllermeier, E., Senge, R., & Donner-Banzhoff, N. (2013). Diagnose im Kontext - eine erweiterte Perspektive. Zeitschrift Für Evidenz, Fortbildung Und Qualität Im Gesundheitswesen (ZEFQ), 107, 585–591.
LibreCat
2013 | Journal Article | LibreCat-ID: 16123
Shaker, A., Senge, R., & Hüllermeier, E. (2013). Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences, 220, 34–45.
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2013 | Conference Paper | LibreCat-ID: 13115
Szarvas, G., Busa-Fekete, R., & Hüllermeier, E. (2013). Learning to rank lexical substitutions. In In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA.
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2013 | Conference Paper | LibreCat-ID: 13116
Dembczynski, K., Jachnik, A., Kotlowski, W., Waegeman, W., & Hüllermeier, E. (2013). Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization. In S. Dasgupta & D. McAllester (Eds.), in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA (pp. 1130–1138).
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2013 | Conference Paper | LibreCat-ID: 13117
Busa-Fekete, R., Szoreny, B., Weng, P., Cheng, W., & Hüllermeier, E. (2013). Top-k selection based on adaptive sampling of noisy preferences. In S. Dasgupta & D. McAllester (Eds.), in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA (pp. 1094–1102).
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2013 | Conference Paper | LibreCat-ID: 13118
Hüllermeier, E., & Cheng, W. (2013). Preference-based CBR: General ideas and basic principles. In F. Rossi (Ed.), in Proceedings IJCAI-13, 23rd international Joint Conference on Artificial Intelligence, Beijing, China (pp. 3012–3016). AAAI Press.
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2013 | Conference Paper | LibreCat-ID: 13119
Henzgen, S., Strickert, M., & Hüllermeier, E. (2013). Rule chains for visualizing evolving fuzzy rule-based systems. In R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, & A. Zolnierek (Eds.), in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland (pp. 279–288). Springer.
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2013 | Conference Paper | LibreCat-ID: 13190
Shaker, A., & Hüllermeier, E. (2013). Recovery analysis for adaptive learning from non-stationary data streams. In R. Burduk, K. Jackowski, M. Kurzynski, W. Wozniak, & A. Zolnierek (Eds.), in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland (pp. 289–298). Springer.
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2012 | Conference Paper | LibreCat-ID: 15299
Leinweber, M., Baumgärtner, L., Mernberger, M., Fober, T., Hüllermeier, E., Klebe, G., & Freisleben, B. (2012). GPU-based cloud computing for comparing the structure of protein binding sites. In in Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione d`Italia, Italy.
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2012 | Book Chapter | LibreCat-ID: 15396
Hüllermeier, E., & Fallah Tehrani, A. (2012). Efficient learning of classifiers based on the 2-additive Choquet integral. In C. Moewes & A. Nürnberger (Eds.), Computational Intelligence in Intelligent Data Analysis (pp. 17–30). Springer.
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2012 | Conference Paper | LibreCat-ID: 15754
Bräuning, M., & Hüllermeier, E. (2012). Learning conditional lexicographic preference trees. In In Workshops on Preference Learning at ECAI, European Conference on Artiticial intelligence, Montpellier, France.
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2012 | Conference Paper | LibreCat-ID: 15114
Hüllermeier, E., & Fallah Tehrani, A. (2012). On the VC dimension of the Choquet integral. In In Proceedings IPMU-2012 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Catania, Italy.
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2012 | Journal Article | LibreCat-ID: 16084
Fallah Tehrani, A., Cheng, W., Dembczynski, K., & Hüllermeier, E. (2012). Learning monotone nonlinear models using the Choquet integral. Machine Learning, 89(1), 183–211.
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2012 | Journal Article | LibreCat-ID: 16085
Bustince, H., Pagola, M., Mesiar, R., Hüllermeier, E., & Herrera, F. (2012). Grouping, overlap and generalized bientropic functions for fuzzy modeling of pairwise comparisons. IEEE Transactions on Fuzzy Systems, 20(3), 405–415.
LibreCat
2012 | Journal Article | LibreCat-ID: 16087
Fürnkranz, J., Hüllermeier, E., Cheng, W., & Park, S. H. (2012). Preference-based reinforcement learning: A formal framework and a policy iteration algorithm. Machine Learning, 89(1), 123–156.
LibreCat
2012 | Journal Article | LibreCat-ID: 16088
Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2012). On label dependence and loss minimization in multi-label classification. Machine Learning, 88(1–2), 5–45.
LibreCat
2012 | Journal Article | LibreCat-ID: 16089
Shaker, A., & Hüllermeier, E. (2012). IBL Streams: A system for instance-based classification and regression on data streams. Evolving Systems, 3(4), 235–249.
LibreCat
2012 | Journal Article | LibreCat-ID: 16090
Dolorez Ruiz, M., & Hüllermeier, E. (2012). A formal and empirical analysis of the fuzzy gamma rank correlation coefficient. Information Sciences, 206, 1–17.
LibreCat
2012 | Journal Article | LibreCat-ID: 16091
Fallah Tehrani, A., Cheng, W., & Hüllermeier, E. (2012). Preference learning using the Choquet integral: The case of multipartite ranking. IEEE Transactions on Fuzzy Systems, 20(6), 1102–1113.
LibreCat
2012 | Journal Article | LibreCat-ID: 16092
Fober, T., Mernberger, M., Klebe, G., & Hüllermeier, E. (2012). Fingerprint kernels for protein structure comparison. Molecular Informatics, 31(6–7), 443–452.
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2012 | Journal Article | LibreCat-ID: 16093
Hüllermeier, E., Rifqi, M., Henzgen, S., & Senge, R. (2012). Comparing fuzzy partitions: A generalization of the Rand index and related measures. IEEE Transactions on Fuzzy Systems, 20(3), 546–556.
LibreCat
2012 | Journal Article | LibreCat-ID: 16094
Senge, R., Fober, T., Nasiri, N., & Hüllermeier, E. (2012). Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung. At-Atomatisierungstechnik, 60(10), 622–629.
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2012 | Conference Paper | LibreCat-ID: 13120
Cheng, W., Hüllermeier, E., Waegeman, W., & Welker, V. (2012). Label ranking with partial abstention based on thresholded probalistic models. In In Proceedings NIPS-2012, 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA.
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2012 | Conference Paper | LibreCat-ID: 13191
Cheng, W., & Hüllermeier, E. (2012). Probability estimation for mulit-class classification based on label ranking. In Proceedings ECML/PKDD-2012, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, UK.
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2012 | Conference Paper | LibreCat-ID: 13192
Dembczynski, K., Kotlowski, W., & Hüllermeier, E. (2012). Consistent multilabel ranking through univariate loss minimization. In J. Langford & J. Pineau (Eds.), in Proceedings ICML-2012, International Conference on Machine Learning, Edinburgh, Scotland.
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2012 | Conference Paper | LibreCat-ID: 13193
Dembczynski, K., Waegeman, W., & Hüllermeier, E. (2012). An analysis of chaining in multi-label classification. In In Proceedings ECAI-2012, 20th European Conference on Artificial Inteligence, Montpellier, France (pp. 294–299). IOS Press.
LibreCat
2012 | Book Chapter | LibreCat-ID: 10153
Hüllermeier, E. (2012). Fuzzy rules in data mining: From fuzzy associations to gradual dependencies. In E. Trillas, P. P. Bonissone, L. Magdalena, & J. Kacprzyk (Eds.), Combining Experimentation and Theory (Vol. 271, pp. 123–135). Springer.
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2011 | Conference Paper | LibreCat-ID: 15762
Dembczynski, K., Waegeman, W., & Hüllermeier, E. (2011). Joint mode estimation in multi-label classification by chaining. In In Proceedings ECML Workshop on Collective inference and Learning on Structured Data, Athens, Greece.
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2011 | Conference Paper | LibreCat-ID: 15764
Mernberger, M., Moog, D., Stork, S., Zauner, S., Maier, U., & Hüllermeier, E. (2011). Prediction of protein localization for specialized compartments using time series kernels. In In Proceedings GCB-2011 German Conference on Bioinformatics Munich, Germany.
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2011 | Conference Paper | LibreCat-ID: 15115
Nasiri, M., Hüllermeier, E., Senge, R., & Lughofer, E. (2011). Comparing methods for knowledge-driven and data-driven fuzzy modeling: A case study in textile industry. In in Proceedings IFSA-2011 World Congress of the International Fuzzy Systems Association, Surabaya and Bali Island, Indonesia (p. RW-103-1-6).
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2011 | Conference Paper | LibreCat-ID: 15116
Lughofer, E., & Hüllermeier, E. (2011). On-line redundancy deletion in evolving fuzzy regression models using a fuzzy inclusion measure. In S. Galichet, J. Montero, & G. Mauris (Eds.), in Proceedings EUSFLAT-2011 7th International Conference on the European Society for Fuzzy Logic and Technology, Aix-les-Bains, France (pp. 380–387).
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2011 | Conference Paper | LibreCat-ID: 15117
Fallah Tehrani, A., Cheng, W., & Hüllermeier, E. (2011). Choquistic regression: Generalizing logistic regression using the Choquet integral. In in Proceedings EUSFLAT-2011 7th International Conference on the European Society for Fuzzy Logic and Technology, Aix-les-Bains, France (pp. 868–875).
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2011 | Journal Article | LibreCat-ID: 16119
Hirsch, O., Bösner, S., Hüllermeier, E., Senge, R., Dembczynski, K., & Donner-Banzhoff, N. (2011). Multivariate modeling to identify patterns in clinical data: The example of chest pain. BMC Medical Research Methodology, 11(155).
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2011 | Journal Article | LibreCat-ID: 16126
Domshlak, C., Hüllermeier, E., Kaci, S., & Prade, H. (2011). Preferences in AL: An overview. Artificial Intelligence, 175(7–8), 1037–1052.
LibreCat
2011 | Journal Article | LibreCat-ID: 16147
Hüllermeier, E. (2011). Fuzzy machine learning and data mining. WIREs Data Mining and Knowledge Discovery, 1(4), 269–283.
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2011 | Journal Article | LibreCat-ID: 16148
Mernberger, M., Klebe, G., & Hüllermeier, E. (2011). SEGA: Semi-global graph alignment for structure-based protein comparison. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 8(5), 1330–1343.
LibreCat
2011 | Journal Article | LibreCat-ID: 16149
Fober, T., Glinca, S., Klebe, G., & Hüllermeier, E. (2011). Superposition and alignment of labeled point clouds. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6), 1653–1666.
LibreCat
2011 | Journal Article | LibreCat-ID: 16150
Hüllermeier, E. (2011). Fuzzy sets in machine learning and data mining. Applied Soft Computing Journal, 1493–1505.
LibreCat
2011 | Journal Article | LibreCat-ID: 16153
Senge, R., & Hüllermeier, E. (2011). Top-down induction of fuzzy pattern trees. IEEE Transactions on Fuzzy Systems, 19(2), 241–252.
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2011 | Conference Paper | LibreCat-ID: 13194
Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2011). An exact algorithm for F-measure maximization. In In Proceedings NIPS-2011, 25th Annual Conference on Neural Information Processing Systems, Granada, Spain.
LibreCat
2011 | Conference Paper | LibreCat-ID: 13195
Cheng, W., Fuernkranz, J., Hüllermeier, E., & Park, S. H. (2011). Preference-based policy iteration: Leveraging preference learning for reinforcement learning. In Proceedings ECML/PKDD-2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece, .
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2011 | Conference Paper | LibreCat-ID: 13196
Fürnkranz, J., & Hüllermeier, E. (2011). Learning from label preferences. In T. Elomaa, J. Hollmen, & H. Mannila (Eds.), in Proceedings DS-2011, 14th International Conference on Discovery Science, number 6926 in LNAI (pp. 2–17). Springer.
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2011 | Conference Paper | LibreCat-ID: 13197
Fallah Tehrani, A., Cheng, W., Dembczynski, K., & Hüllermeier, E. (2011). Learning monotone nonlinear models using the Choquet integral. In In Proceedings ECML/PKDD-2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece,.
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2011 | Conference Paper | LibreCat-ID: 13198
Hüllermeier, E., & Schlegel, P. (2011). Preference-based CBR: First steps toward a methodological framework. In A. Ram & N. Wiratunga (Eds.), In Proceedings ICCBR-2011, 19th International Conference on Case-Based Reasoning, number 6880 in LNAI (pp. 77–91). Springer.
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2011 | Conference Paper | LibreCat-ID: 13588
Kotlowski, W., Dembczynski, K., & Hüllermeier, E. (2011). Bipartite ranking through minimization of univariate loss. In in Proceedings ICML-2011, 28th International Conference on Machine Learning, Washington, USA.
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2010 | Book Chapter | LibreCat-ID: 15297
Hüllermeier, E., & Hühn, J. (2010). An Analysis of the FURIA algorithm for fuzzy rule induction. In J. Koronacki, Z. W. Ras, S. T. Wierzchon, & J. Kacprzyk (Eds.), Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S.Michalski (Vol. 262, pp. 321–344). Springer.
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2010 | Book Chapter | LibreCat-ID: 15298
Fürnkranz, J., & Hüllermeier, E. (2010). Preference Learning: An Introduction. In J. Fürnkranz & E. Hüllermeier (Eds.), Preference Learning (pp. 1–18). Springer.
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2010 | Conference Paper | LibreCat-ID: 15765
Fober, T., Mernberger, M., Klebe, G., & Hüllermeier, E. (2010). Efficient similarity retrieval for protein binding sites based on histogram comparison. In In Proceedings GCB-2010 German Conference on Bioinformatics Braunschweig, Germany (pp. 51–60).
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2010 | Conference Paper | LibreCat-ID: 15772
Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2010). On label dependence in multi-label classification. In In Proceedings MLD-2010, 2nd International Workshop on Learning from Multi-Label Data, Haifa, Israel.
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2010 | Conference Paper | LibreCat-ID: 15118
Fober, T., & Hüllermeier, E. (2010). Similarity measures for protein structures based on fuzzy histogram comparison. In in Proceedings WCCI-2010, World Congress on Computational Intelligence, Barcelona, Spain.
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2010 | Conference Paper | LibreCat-ID: 15119
Senge, R., & Hüllermeier, E. (2010). Pattern trees for regression and fuzzy systems modeling. In in Proceedings WCCI-2010, World Congress on Computational Intelligence, Barcelona, Spain.
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2010 | Journal Article | LibreCat-ID: 16121
Pfeffer, P., Fober, T., Hüllermeier, E., & Klebe, G. (2010). GARLig: A fully automated tool for subset selection of large fragment spaces via a self-adaptive genetic algorithm. Journal of Chemical Information and Modeling, 50(9), 1644–1659.
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2010 | Journal Article | LibreCat-ID: 16151
Hüllermeier, E., & Vanderlooy, S. (2010). Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognition, 43(1), 128–142.
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2010 | Journal Article | LibreCat-ID: 16152
Hüllermeier, E., & Fürnkranz, J. (2010). On predictive accuracy and risk minimization in pairwise label ranking. Journal of Computer and System Sciences, 76(1), 49–62.
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2010 | Conference Paper | LibreCat-ID: 13589
Dembczynski, K., Cheng, W., & Hüllermeier, E. (2010). Bayes optimal multilabel classification via probalistic classifier chains. In J. Fürnkranz & T. Joachims (Eds.), in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel (pp. 279–286).
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2010 | Conference Paper | LibreCat-ID: 13590
Cheng, W., Dembczynski, K., & Hüllermeier, E. (2010). Label ranking based on the Plackett-Luce model. In J. Fürnkranz & T. Joachims (Eds.), in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel (pp. 215–222).
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2010 | Conference Paper | LibreCat-ID: 13591
Cheng, W., Dembczynski, K., & Hüllermeier, E. (2010). Graded multi-label classification: The ordinal case. In J. Fürnkranz & T. Joachims (Eds.), in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel.
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2010 | Conference Paper | LibreCat-ID: 13593
Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2010). Regret analysis for performance metrics in multi-label classification: The case of Hamming and subset zero-one loss. In In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain.
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2010 | Conference Paper | LibreCat-ID: 13594
Cheng, W., Rademaker, M., De Beats, B., & Hüllermeier, E. (2010). Predicting partial orders: Ranking with abstention. In In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain.
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2010 | Conference Paper | LibreCat-ID: 13597
Hüllermeier, E. (2010). Uncertainty in clustering and classification . In in Proceedings SUM 2010, International Conference on Scalable Uncertainty Management (Vol. 6379, pp. 16–19). Springer.
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2009 | Conference Paper | LibreCat-ID: 15773
Cheng, W., & Hüllermeier, E. (2009). A simple instance-based approach to multilabel classification using the Mallows model. In In Proceedings MLD-2009 1st International Workshop on Learning from Multi-Label Data, Bled, Slovenia (pp. 28–38).
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2009 | Conference Paper | LibreCat-ID: 15774
Senge, R., & Hüllermeier, E. (2009). Learning pattern tree classifiers using a co-evolutionary algorithm. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 19th Workshop Computational Intelligence, Dortmund Germany (pp. 22–33). KIT Scientific Publishing.
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2009 | Conference Paper | LibreCat-ID: 15775
Fober, T., Mernberger, M., Moritz, R., & Hüllermeier, E. (2009). Graph-kernels for the comparative analysis of protein active sites. In I. Grosse, S. Neumann, S. Posch, F. Schreiber, & P. . Stadler (Eds.), In Proceedings GCB-2009 German Conference on Bioinformatics Halle (Saale), Germany (pp. 21–31).
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2009 | Conference Paper | LibreCat-ID: 15776
Senge, R., & Hüllermeier, E. (2009). Learning pattern tree classifiers using a co-evolutionary algorithm. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Darmstadt, Germany (pp. 105–110).
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2009 | Conference Paper | LibreCat-ID: 15778
Fober, T., Mernberger, M., Melnikov, V., Moritz, R., & Hüllermeier, E. (2009). Extension and empirical comparison of graph-kernels for the analysis of protein active sites. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Darmstadt, Germany (pp. 30–36).
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2009 | Conference Paper | LibreCat-ID: 15120
Fober, T., Klebe, G., & Hüllermeier, E. (2009). Efficient construction of multiple geometrical alignments for the comparison of protein binding sites. In in Proceedings ISDA-2009, 9th international Conference on Intelligent Systems Design and Applications, Pisa, Italy (pp. 1251–1256).
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2009 | Conference Paper | LibreCat-ID: 15194
Boukhris, I., Elouedi, Z., Fober, T., Mernberger, M., & Hüllermeier, E. (2009). Similarity analysis of protein binding sites: A generalization of the maximum common subgraph measure based on quasi-clique detection. In In Proceedings IDA-2009, 9th International Symposium on Intelligent Data Analysis, Pisa / Italy (pp. 1245–1250).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15195
Fober, T., & Hüllermeier, E. (2009). Fuzzy modeling of labeled point cloud superposition for the comparison of protein binding sites. In in Proceedings IFSA / EUSFLAT-2009 World Congress of the Fuzzy Systems Association, Lissabon, Portugal (pp. 1299–1304).
LibreCat
2009 | Conference Paper | LibreCat-ID: 15196
Hüllermeier, E., & Rifqi, M. (2009). A fuzzy variant of the Rand index for comparing clustering structures. In in Proceedings IFSA / EUSFLAT-2009 World Congress of the Fuzzy Systems Association, Lissabon, Portugal (pp. 1294–1298).
LibreCat
2009 | Journal Article | LibreCat-ID: 16154
Weskamp, N., Hüllermeier, E., & Klebe, G. (2009). Merging chemical and biological space: Structural mapping of enzyme binding pocket space. Proteins, 76(2), 317–330.
LibreCat
2009 | Journal Article | LibreCat-ID: 16158
Hühn, J., & Hüllermeier, E. (2009). FURIA: An Algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery , 19, 293–319.
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2009 | Journal Article | LibreCat-ID: 16159
Hüllermeier, E., & Vanderlooy, S. (2009). Why fuzzy decision trees are good rankers. IEEE Transactions on Fuzzy Systems, 17(6), 1233–1244.
LibreCat
2009 | Journal Article | LibreCat-ID: 16160
Cheng, W., & Hüllermeier, E. (2009). Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, 76(2–3), 211–225.
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2009 | Journal Article | LibreCat-ID: 16161
Yi, Y., Fober, T., & Hüllermeier, E. (2009). Fuzzy operator trees for modeling rating functions. International Journal of Computational Intelligence and Applications, 8(4), 413–428.
LibreCat
2009 | Journal Article | LibreCat-ID: 16162
Fober, T., Mernberger, M., Klebe, G., & Hüllermeier, E. (2009). Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. Bioinformatics, 25(16), 2110–2117.
LibreCat
2009 | Journal Article | LibreCat-ID: 16163
Hühn, J., & Hüllermeier, E. (2009). FR3: A fuzzy rule learner for inducing reliable classifiers. IEEE Transactions on Fuzzy Systems, 17(1), 138–149.
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2009 | Journal Article | LibreCat-ID: 16165
Hüllermeier, E., Vladimirskiy, I., Prados Suarez, B., & Stauch, E. (2009). Supporting case-based retrieval by similarity skylines: Basic concepts and extensions. Künstliche Intelligenz, 1(09), 24–29.
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