439 Publications

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[439]
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
 
[438]
2023 | Preprint | LibreCat-ID: 44512 | OA
S. Uhlemeyer, J. Lienen, E. Hüllermeier, and H. Gottschalk, “Detecting Novelties with Empty Classes,” arXiv:2305.00983. 2023.
LibreCat | Download (ext.) | arXiv
 
[437]
2023 | Conference Paper | LibreCat-ID: 31880 | OA
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.
LibreCat | Download (ext.)
 
[436]
2023 | Book Chapter | LibreCat-ID: 45884 | OA
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.
LibreCat | Files available | DOI
 
[435]
2023 | Book Chapter | LibreCat-ID: 45886 | OA
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.
LibreCat | Files available | DOI
 
[434]
2023 | Preprint | LibreCat-ID: 45911 | OA
J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,” arXiv:2305.13764. 2023.
LibreCat | Download (ext.) | arXiv
 
[433]
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.
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[432]
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
 
[431]
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
 
[430]
2023 | Book Chapter | LibreCat-ID: 48778
M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, and E. Huellermeier, “iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios,” in Communications in Computer and Information Science, Cham: Springer Nature Switzerland, 2023.
LibreCat | DOI
 
[429]
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.
LibreCat | DOI
 
[428]
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
 
[427]
2022 | Preprint | LibreCat-ID: 30868
E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022.
LibreCat | arXiv
 
[426]
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
 
[425]
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
 
[424]
2022 | Preprint | LibreCat-ID: 31546 | OA
J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised Learning,” arXiv:2205.15239. 2022.
LibreCat | Download (ext.)
 
[423]
2022 | Preprint | LibreCat-ID: 30867
A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.
LibreCat | arXiv
 
[422]
2022 | Preprint | LibreCat-ID: 30865
A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022.
LibreCat | arXiv
 
[421]
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.
LibreCat | DOI
 
[420]
2022 | Report | LibreCat-ID: 36227
B. Hammer et al., Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022.
LibreCat | DOI
 
[419]
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.
LibreCat | DOI
 
[418]
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
 
[417]
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
 
[416]
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.
LibreCat | DOI
 
[415]
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
 
[414]
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
 
[413]
2021 | Journal Article | LibreCat-ID: 21636
J. Lienen and E. Hüllermeier, “Instance weighting through data imprecisiation,” International Journal of Approximate Reasoning, 2021.
LibreCat | Download (ext.)
 
[412]
2021 | Conference Paper | LibreCat-ID: 21637 | OA
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.
LibreCat | Download (ext.)
 
[411]
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.
LibreCat | Download (ext.)
 
[410]
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
 
[409]
2021 | Preprint | LibreCat-ID: 22509 | OA
J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” arXiv:2106.11853. 2021.
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[408]
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
 
[407]
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
 
[406]
2021 | Preprint | LibreCat-ID: 30866
T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” arXiv:2111.05850. 2021.
LibreCat | arXiv
 
[405]
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
 
[404]
2021 | Book Chapter | LibreCat-ID: 29292 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[403]
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
 
[402]
2021 | Journal Article | LibreCat-ID: 24456 | OA
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.
LibreCat | Files available | DOI
 
[401]
2020 | Preprint | LibreCat-ID: 19603 | OA
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.
LibreCat | Download (ext.)
 
[400]
2020 | Conference Paper | LibreCat-ID: 19953 | OA
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
 
[399]
2020 | Preprint | LibreCat-ID: 20211 | OA
J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model,” arXiv:2010.13118. 2020.
LibreCat | Download (ext.)
 
[398]
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
 
[397]
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
 
[396]
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
 
[395]
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
 
[394]
2020 | Preprint | LibreCat-ID: 17605 | OA
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.
LibreCat | Download (ext.)
 
[393]
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
 
[392]
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
 
[391]
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
 
[390]
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.
LibreCat | Download (ext.)
 
[389]
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
 
[388]
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
 
[387]
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.
LibreCat | Files available
 
[386]
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
 
[385]
2019 | Journal Article | LibreCat-ID: 15001
I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning,” IEEE Computational Intelligence Magazine, pp. 31–44, 2019.
LibreCat | DOI
 
[384]
2019 | Journal Article | LibreCat-ID: 15002 | OA
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.
LibreCat | Files available | DOI
 
[383]
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
 
[382]
2019 | Book Chapter | LibreCat-ID: 15004
M. Ahmadi Fahandar and E. Hüllermeier, “Feature Selection for Analogy-Based Learning to Rank,” in Discovery Science, Cham, 2019.
LibreCat | DOI
 
[381]
2019 | Book Chapter | LibreCat-ID: 15005
M. Ahmadi Fahandar and E. Hüllermeier, “Analogy-Based Preference Learning with Kernels,” in KI 2019: Advances in Artificial Intelligence, Cham, 2019.
LibreCat | DOI
 
[380]
2019 | Book Chapter | LibreCat-ID: 15006
V.-L. Nguyen, S. Destercke, and E. Hüllermeier, “Epistemic Uncertainty Sampling,” in Discovery Science, Cham, 2019.
LibreCat | DOI
 
[379]
2019 | Conference Paper | LibreCat-ID: 15007 | OA
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.
LibreCat | Files available | DOI
 
[378]
2019 | Conference Paper | LibreCat-ID: 15011 | OA
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
 
[377]
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
 
[376]
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
 
[375]
2019 | Journal Article | LibreCat-ID: 15015
S. Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on Knowledge Discovery from Data, pp. 1–36, 2019.
LibreCat | DOI
 
[374]
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
 
[373]
2019 | Conference Paper | LibreCat-ID: 10232 | OA
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.
LibreCat | Files available
 
[372]
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.
LibreCat | DOI
 
[371]
2018 | Conference Paper | LibreCat-ID: 2479 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[370]
2018 | Conference Paper | LibreCat-ID: 2857 | OA
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.
LibreCat | Files available | Download (ext.)
 
[369]
2018 | Conference Paper | LibreCat-ID: 2471 | OA
F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
LibreCat | Files available | DOI | Download (ext.)
 
[368]
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.
LibreCat | Files available | DOI
 
[367]
2018 | Journal Article | LibreCat-ID: 3510 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[366]
2018 | Conference Paper | LibreCat-ID: 3552 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[365]
2018 | Conference Paper | LibreCat-ID: 3852 | OA
M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
LibreCat | Files available | Download (ext.)
 
[364]
2018 | Conference Paper | LibreCat-ID: 2109 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[363]
2018 | Preprint | LibreCat-ID: 17713 | OA
M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.
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[362]
2018 | Preprint | LibreCat-ID: 17714 | OA
F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.
LibreCat | Download (ext.)
 
[361]
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.
LibreCat | Files available | DOI
 
[360]
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
 
[359]
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
 
[358]
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
 
[357]
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
 
[356]
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
 
[355]
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
 
[354]
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
 
[353]
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
 
[352]
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
 
[351]
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
 
[350]
2018 | Conference Abstract | LibreCat-ID: 1379 | OA
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.
LibreCat | Files available | Download (ext.)
 
[349]
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
 
[348]
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
 
[347]
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
 
[346]
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
 
[345]
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
 
[344]
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
 
[343]
2017 | Conference Paper | LibreCat-ID: 1180 | OA
M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.
LibreCat | Files available | Download (ext.)
 
[342]
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
 
[341]
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
 
[340]
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
 
[339]
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
 
[338]
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
 
[337]
2017 | Conference Paper | LibreCat-ID: 10206 | OA
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
 
[336]
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
 
[335]
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
 
[334]
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
 
[333]
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
 
[332]
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
 
[331]
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
 
[330]
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
 
[329]
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
 
[328]
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
 
[327]
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
 
[326]
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 | Files available | DOI
 
[325]
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
 
[324]
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
 
[323]
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
 
[322]
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
 
[321]
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
 
[320]
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
 
[319]
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
 
[318]
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
 
[317]
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
 
[316]
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
 
[315]
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
 
[314]
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
 
[313]
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
 
[312]
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
 
[311]
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
 
[310]
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
 
[309]
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
 
[308]
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
 
[307]
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
 
[306]
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
 
[305]
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
 
[304]
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
 
[303]
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
 
[302]
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
 
[301]
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 | Files available | DOI
 
[300]
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
 
[299]
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
 
[298]
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
 
[297]
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
 
[296]
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
 
[295]
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
 
[294]
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
 
[293]
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
 
[292]
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
 
[291]
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
 
[290]
2015 | Conference Paper | LibreCat-ID: 10235
F. Hoffmann and E. Hüllermeier, “Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing,” 2015.
LibreCat
 
[289]
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
 
[288]
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
 
[287]
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
 
[286]
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
 
[285]
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
 
[284]
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
 
[283]
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
 
[282]
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
 
[281]
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
 
[280]
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
 
[279]
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
 
[278]
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
 
[277]
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
 
[276]
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
 
[275]
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
 
[274]
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
 
[273]
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
 
[272]
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
 
[271]
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
 
[270]
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
 
[269]
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
 
[268]
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
 
[267]
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
 
[266]
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
 
[265]
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
 
[264]
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
 
[263]
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
 
[262]
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
 
[261]
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
 
[260]
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
 
[259]
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
 
[258]
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
 
[257]
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
 
[256]
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
 
[255]
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
 
[254]
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
 
[253]
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
 
[252]
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
 
[251]
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
 
[250]
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
 
[249]
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
 
[248]
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
 
[247]
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
 
[246]
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
 
[245]
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
 
[244]
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
 
[243]
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
 
[242]
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
 
[241]
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
 
[240]
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.
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[439]
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
 
[438]
2023 | Preprint | LibreCat-ID: 44512 | OA
S. Uhlemeyer, J. Lienen, E. Hüllermeier, and H. Gottschalk, “Detecting Novelties with Empty Classes,” arXiv:2305.00983. 2023.
LibreCat | Download (ext.) | arXiv
 
[437]
2023 | Conference Paper | LibreCat-ID: 31880 | OA
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.
LibreCat | Download (ext.)
 
[436]
2023 | Book Chapter | LibreCat-ID: 45884 | OA
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.
LibreCat | Files available | DOI
 
[435]
2023 | Book Chapter | LibreCat-ID: 45886 | OA
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.
LibreCat | Files available | DOI
 
[434]
2023 | Preprint | LibreCat-ID: 45911 | OA
J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,” arXiv:2305.13764. 2023.
LibreCat | Download (ext.) | arXiv
 
[433]
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.
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[432]
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
 
[431]
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
 
[430]
2023 | Book Chapter | LibreCat-ID: 48778
M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, and E. Huellermeier, “iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios,” in Communications in Computer and Information Science, Cham: Springer Nature Switzerland, 2023.
LibreCat | DOI
 
[429]
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.
LibreCat | DOI
 
[428]
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
 
[427]
2022 | Preprint | LibreCat-ID: 30868
E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022.
LibreCat | arXiv
 
[426]
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
 
[425]
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
 
[424]
2022 | Preprint | LibreCat-ID: 31546 | OA
J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised Learning,” arXiv:2205.15239. 2022.
LibreCat | Download (ext.)
 
[423]
2022 | Preprint | LibreCat-ID: 30867
A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.
LibreCat | arXiv
 
[422]
2022 | Preprint | LibreCat-ID: 30865
A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022.
LibreCat | arXiv
 
[421]
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.
LibreCat | DOI
 
[420]
2022 | Report | LibreCat-ID: 36227
B. Hammer et al., Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022.
LibreCat | DOI
 
[419]
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.
LibreCat | DOI
 
[418]
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
 
[417]
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
 
[416]
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.
LibreCat | DOI
 
[415]
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
 
[414]
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
 
[413]
2021 | Journal Article | LibreCat-ID: 21636
J. Lienen and E. Hüllermeier, “Instance weighting through data imprecisiation,” International Journal of Approximate Reasoning, 2021.
LibreCat | Download (ext.)
 
[412]
2021 | Conference Paper | LibreCat-ID: 21637 | OA
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.
LibreCat | Download (ext.)
 
[411]
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.
LibreCat | Download (ext.)
 
[410]
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
 
[409]
2021 | Preprint | LibreCat-ID: 22509 | OA
J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” arXiv:2106.11853. 2021.
LibreCat | Download (ext.)
 
[408]
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
 
[407]
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
 
[406]
2021 | Preprint | LibreCat-ID: 30866
T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” arXiv:2111.05850. 2021.
LibreCat | arXiv
 
[405]
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
 
[404]
2021 | Book Chapter | LibreCat-ID: 29292 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[403]
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
 
[402]
2021 | Journal Article | LibreCat-ID: 24456 | OA
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.
LibreCat | Files available | DOI
 
[401]
2020 | Preprint | LibreCat-ID: 19603 | OA
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.
LibreCat | Download (ext.)
 
[400]
2020 | Conference Paper | LibreCat-ID: 19953 | OA
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
 
[399]
2020 | Preprint | LibreCat-ID: 20211 | OA
J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model,” arXiv:2010.13118. 2020.
LibreCat | Download (ext.)
 
[398]
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
 
[397]
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
 
[396]
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
 
[395]
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
 
[394]
2020 | Preprint | LibreCat-ID: 17605 | OA
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.
LibreCat | Download (ext.)
 
[393]
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
 
[392]
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
 
[391]
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
 
[390]
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.
LibreCat | Download (ext.)
 
[389]
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
 
[388]
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
 
[387]
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.
LibreCat | Files available
 
[386]
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
 
[385]
2019 | Journal Article | LibreCat-ID: 15001
I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning,” IEEE Computational Intelligence Magazine, pp. 31–44, 2019.
LibreCat | DOI
 
[384]
2019 | Journal Article | LibreCat-ID: 15002 | OA
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.
LibreCat | Files available | DOI
 
[383]
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
 
[382]
2019 | Book Chapter | LibreCat-ID: 15004
M. Ahmadi Fahandar and E. Hüllermeier, “Feature Selection for Analogy-Based Learning to Rank,” in Discovery Science, Cham, 2019.
LibreCat | DOI
 
[381]
2019 | Book Chapter | LibreCat-ID: 15005
M. Ahmadi Fahandar and E. Hüllermeier, “Analogy-Based Preference Learning with Kernels,” in KI 2019: Advances in Artificial Intelligence, Cham, 2019.
LibreCat | DOI
 
[380]
2019 | Book Chapter | LibreCat-ID: 15006
V.-L. Nguyen, S. Destercke, and E. Hüllermeier, “Epistemic Uncertainty Sampling,” in Discovery Science, Cham, 2019.
LibreCat | DOI
 
[379]
2019 | Conference Paper | LibreCat-ID: 15007 | OA
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.
LibreCat | Files available | DOI
 
[378]
2019 | Conference Paper | LibreCat-ID: 15011 | OA
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
 
[377]
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
 
[376]
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
 
[375]
2019 | Journal Article | LibreCat-ID: 15015
S. Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on Knowledge Discovery from Data, pp. 1–36, 2019.
LibreCat | DOI
 
[374]
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
 
[373]
2019 | Conference Paper | LibreCat-ID: 10232 | OA
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.
LibreCat | Files available
 
[372]
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.
LibreCat | DOI
 
[371]
2018 | Conference Paper | LibreCat-ID: 2479 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[370]
2018 | Conference Paper | LibreCat-ID: 2857 | OA
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.
LibreCat | Files available | Download (ext.)
 
[369]
2018 | Conference Paper | LibreCat-ID: 2471 | OA
F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
LibreCat | Files available | DOI | Download (ext.)
 
[368]
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.
LibreCat | Files available | DOI
 
[367]
2018 | Journal Article | LibreCat-ID: 3510 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[366]
2018 | Conference Paper | LibreCat-ID: 3552 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[365]
2018 | Conference Paper | LibreCat-ID: 3852 | OA
M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
LibreCat | Files available | Download (ext.)
 
[364]
2018 | Conference Paper | LibreCat-ID: 2109 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[363]
2018 | Preprint | LibreCat-ID: 17713 | OA
M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.
LibreCat | Download (ext.)
 
[362]
2018 | Preprint | LibreCat-ID: 17714 | OA
F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.
LibreCat | Download (ext.)
 
[361]
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.
LibreCat | Files available | DOI
 
[360]
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
 
[359]
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
 
[358]
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
 
[357]
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
 
[356]
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
 
[355]
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
 
[354]
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
 
[353]
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
 
[352]
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
 
[351]
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
 
[350]
2018 | Conference Abstract | LibreCat-ID: 1379 | OA
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.
LibreCat | Files available | Download (ext.)
 
[349]
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
 
[348]
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
 
[347]
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
 
[346]
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
 
[345]
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
 
[344]
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
 
[343]
2017 | Conference Paper | LibreCat-ID: 1180 | OA
M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.
LibreCat | Files available | Download (ext.)
 
[342]
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
 
[341]
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
 
[340]
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
 
[339]
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
 
[338]
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
 
[337]
2017 | Conference Paper | LibreCat-ID: 10206 | OA
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
 
[336]
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
 
[335]
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
 
[334]
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
 
[333]
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
 
[332]
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
 
[331]
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
 
[330]
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
 
[329]
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
 
[328]
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
 
[327]
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
 
[326]
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 | Files available | DOI
 
[325]
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
 
[324]
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
 
[323]
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
 
[322]
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
 
[321]
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
 
[320]
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
 
[319]
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
 
[318]
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
 
[317]
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
 
[316]
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
 
[315]
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
 
[314]
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
 
[313]
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
 
[312]
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
 
[311]
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
 
[310]
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
 
[309]
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
 
[308]
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
 
[307]
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
 
[306]
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
 
[305]
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
 
[304]
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
 
[303]
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
 
[302]
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
 
[301]
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 | Files available | DOI
 
[300]
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
 
[299]
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
 
[298]
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
 
[297]
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
 
[296]
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
 
[295]
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
 
[294]
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
 
[293]
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
 
[292]
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
 
[291]
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
 
[290]
2015 | Conference Paper | LibreCat-ID: 10235
F. Hoffmann and E. Hüllermeier, “Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing,” 2015.
LibreCat
 
[289]
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
 
[288]
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
 
[287]
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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[286]
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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[285]
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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[284]
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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[283]
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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[282]
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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[281]
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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[280]
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.
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[279]
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.
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[278]
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.
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[277]
2015 | Journal Article | LibreCat-ID: 10320
E. Hüllermeier, “Does machine learning need fuzzy logic?,” Fuzzy Sets and Systems, vol. 281, pp. 292–299, 2015.
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[276]
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.
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[275]
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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[274]
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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[273]
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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[272]
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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[271]
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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[270]
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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[269]
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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[268]
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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[267]
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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[266]
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
 
[265]
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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[264]
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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[263]
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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[262]
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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[261]
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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[260]
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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[259]
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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[258]
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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[257]
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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[256]
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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[255]
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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[254]
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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[253]
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.
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[252]
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.
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[251]
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
 
[250]
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.
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[249]
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
 
[248]
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.
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[247]
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
 
[246]
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.
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[245]
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.
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[244]
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.
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[243]
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
 
[242]
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
 
[241]
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.
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[240]
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
 

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