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