Eyke Hüllermeier

eyke@uni-paderborn.de
ID
48129

439 Publications

Mark all

[439]
2024 | Journal Article | LibreCat-ID: 53073
Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence. 2024;38(13):14388-14396. doi:10.1609/aaai.v38i13.29352
LibreCat | DOI
 
[438]
2023 | Preprint | LibreCat-ID: 44512 | OA
Uhlemeyer S, Lienen J, Hüllermeier E, Gottschalk H. Detecting Novelties with Empty Classes. arXiv:230500983. Published online 2023.
LibreCat | Download (ext.) | arXiv
 
[437]
2023 | Conference Paper | LibreCat-ID: 31880 | OA
Nguyen DA, Levie R, Lienen J, Kutyniok G, Hüllermeier E. Memorization-Dilation: Modeling Neural Collapse Under Noise. In: International Conference on Learning Representations, ICLR. ; 2023.
LibreCat | Download (ext.)
 
[436]
2023 | Book Chapter | LibreCat-ID: 45884 | OA
Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104. doi:10.5281/zenodo.8068466
LibreCat | Files available | DOI
 
[435]
2023 | Book Chapter | LibreCat-ID: 45886 | OA
Wehrheim H, Hüllermeier E, Becker S, Becker M, Richter C, Sharma A. Composition Analysis in Unknown Contexts. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:105-123. doi:10.5281/zenodo.8068510
LibreCat | Files available | DOI
 
[434]
2023 | Preprint | LibreCat-ID: 45911 | OA
Lienen J, Hüllermeier E. Mitigating Label Noise through Data Ambiguation. arXiv:230513764. Published online 2023.
LibreCat | Download (ext.) | arXiv
 
[433]
2023 | Journal Article | LibreCat-ID: 21600
Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing. 2023;45(2):A579-A595. doi:10.1137/21M1412682
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[432]
2023 | Conference Paper | LibreCat-ID: 51373
Hanselle JM, Fürnkranz J, Hüllermeier E. Probabilistic Scoring Lists for Interpretable Machine Learning. In: 26th International Conference on Discovery Science . Vol 14050. Lecture Notes in Computer Science. Springer Nature Switzerland; 2023:189-203. doi:10.1007/978-3-031-45275-8_13
LibreCat | DOI
 
[431]
2023 | Book Chapter | LibreCat-ID: 48776
Muschalik M, Fumagalli F, Hammer B, Huellermeier E. iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In: Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-43418-1_26
LibreCat | DOI
 
[430]
2023 | Book Chapter | LibreCat-ID: 48778
Muschalik M, Fumagalli F, Jagtani R, Hammer B, Huellermeier E. iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In: Communications in Computer and Information Science. Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-44064-9_11
LibreCat | DOI
 
[429]
2023 | Conference Paper | LibreCat-ID: 48775
Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. On Feature Removal for Explainability in Dynamic Environments. In: ESANN 2023 Proceedings. i6doc.com publ.; 2023. doi:10.14428/esann/2023.es2023-148
LibreCat | DOI
 
[428]
2023 | Conference Paper | LibreCat-ID: 52230
Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. SHAP-IQ: Unified Approximation of any-order Shapley Interactions. In: NeurIPS 2023 - Advances in Neural Information Processing Systems. Vol 36. Curran Associates, Inc.; 2023:11515--11551.
LibreCat
 
[427]
2022 | Preprint | LibreCat-ID: 30868
Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. arXiv:220201651. Published online 2022.
LibreCat | arXiv
 
[426]
2022 | Conference Paper | LibreCat-ID: 32311
Sharma A, Melnikov V, Hüllermeier E, Wehrheim H. Property-Driven Testing of Black-Box Functions. In: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE). IEEE; 2022:113-123.
LibreCat
 
[425]
2022 | Conference Paper | LibreCat-ID: 34542
Campagner A, Lienen J, Hüllermeier E, Ciucci D. Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. In: Lecture Notes in Computer Science. Vol 13633. Springer; 2022:57-70.
LibreCat
 
[424]
2022 | Preprint | LibreCat-ID: 31546 | OA
Lienen J, Demir C, Hüllermeier E. Conformal Credal Self-Supervised Learning. arXiv:220515239. Published online 2022.
LibreCat | Download (ext.)
 
[423]
2022 | Preprint | LibreCat-ID: 30867
Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. Proceedings of the 36th AAAI Conference on Artificial Intelligence. Published online 2022.
LibreCat | arXiv
 
[422]
2022 | Preprint | LibreCat-ID: 30865
Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. Machine Learning. Published online 2022.
LibreCat | arXiv
 
[421]
2022 | Journal Article | LibreCat-ID: 33090
Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. Published online 2022. doi:10.1007/s40194-022-01339-9
LibreCat | DOI
 
[420]
2022 | Report | LibreCat-ID: 36227
Hammer B, Hüllermeier E, Lohweg V, et al. Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens.; 2022. doi:10.4119/unibi/2965622
LibreCat | DOI
 
[419]
2022 | Journal Article | LibreCat-ID: 48780
Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz. 2022;36(3-4):211-224. doi:10.1007/s13218-022-00766-6
LibreCat | DOI
 
[418]
2021 | Journal Article | LibreCat-ID: 24143
Drees JP, Gupta P, Hüllermeier E, et al. Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! 14th ACM Workshop on Artificial Intelligence and Security. Published online 2021.
LibreCat
 
[417]
2021 | Journal Article | LibreCat-ID: 24148
Ramaswamy A, Hüllermeier E. Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. IEEE Transactions on Artificial Intelligence (to appear). Published online 2021.
LibreCat
 
[416]
2021 | Journal Article | LibreCat-ID: 21004
Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276
LibreCat | DOI
 
[415]
2021 | Journal Article | LibreCat-ID: 21092
Mohr F, Wever MD, Tornede A, Hüllermeier E. 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
Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings of the Genetic and Evolutionary Computation Conference. ; 2021.
LibreCat
 
[413]
2021 | Journal Article | LibreCat-ID: 21636
Lienen J, Hüllermeier E. Instance weighting through data imprecisiation. International Journal of Approximate Reasoning. 2021.
LibreCat | Download (ext.)
 
[412]
2021 | Conference Paper | LibreCat-ID: 21637 | OA
Lienen J, Hüllermeier E. From Label Smoothing to Label Relaxation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI. Vol 35. AAAI Press; 2021:8583-8591.
LibreCat | Download (ext.)
 
[411]
2021 | Conference Paper | LibreCat-ID: 23779
Bernijazov R, Dicks A, Dumitrescu R, et al. A Meta-Review on Artificial Intelligence in Product Creation. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). ; 2021.
LibreCat | Download (ext.)
 
[410]
2021 | Conference Paper | LibreCat-ID: 22280
Lienen J, Hüllermeier E, Ewerth R, Nommensen N. 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. ; 2021:14595-14604.
LibreCat
 
[409]
2021 | Preprint | LibreCat-ID: 22509 | OA
Lienen J, Hüllermeier E. Credal Self-Supervised Learning. arXiv:210611853. 2021.
LibreCat | Download (ext.)
 
[408]
2021 | Conference Paper | LibreCat-ID: 22913
Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021.
LibreCat
 
[407]
2021 | Conference Paper | LibreCat-ID: 27381
Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5
LibreCat | DOI | arXiv
 
[406]
2021 | Preprint | LibreCat-ID: 30866
Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850. Published online 2021.
LibreCat | arXiv
 
[405]
2021 | Conference Paper | LibreCat-ID: 21198
Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.
LibreCat
 
[404]
2021 | Book Chapter | LibreCat-ID: 29292 | OA
Feldhans R, Wilke A, Heindorf S, et al. Drift Detection in Text Data with Document Embeddings. In: Intelligent Data Engineering and Automated Learning – IDEAL 2021. Springer International Publishing; 2021. doi:10.1007/978-3-030-91608-4_11
LibreCat | Files available | DOI | Download (ext.)
 
[403]
2021 | Working Paper | LibreCat-ID: 45616
van Straaten D, Melnikov V, Hüllermeier E, Mir Djawadi B, Fahr R. Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes. Vol 72.; 2021.
LibreCat
 
[402]
2021 | Journal Article | LibreCat-ID: 24456 | OA
Rohlfing KJ, Cimiano P, Scharlau I, 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. 2021;13(3):717-728. doi:10.1109/tcds.2020.3044366
LibreCat | Files available | DOI
 
[401]
2020 | Preprint | LibreCat-ID: 19603 | OA
Bode H, Heid SH, Weber D, Hüllermeier E, Wallscheid O. Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control. arXiv:200504869. 2020.
LibreCat | Download (ext.)
 
[400]
2020 | Conference Paper | LibreCat-ID: 19953 | OA
Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.
LibreCat | Files available | arXiv
 
[399]
2020 | Preprint | LibreCat-ID: 20211 | OA
Lienen J, Hüllermeier E. Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model. arXiv:201013118. 2020.
LibreCat | Download (ext.)
 
[398]
2020 | Conference Paper | LibreCat-ID: 24146
Heid SH, Ramaswamy A, Hüllermeier E. Constrained Multi-Agent Optimization with Unbounded Information Delay. In: Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020. Vol 26. ; 2020:247.
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[397]
2020 | Conference Paper | LibreCat-ID: 17407
Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: Discovery Science. ; 2020.
LibreCat
 
[396]
2020 | Conference Paper | LibreCat-ID: 17408
Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence. ; 2020.
LibreCat
 
[395]
2020 | Conference Paper | LibreCat-ID: 17424
Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020. ; 2020. doi:10.1007/978-3-030-66770-2_8
LibreCat | DOI
 
[394]
2020 | Preprint | LibreCat-ID: 17605 | OA
Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.
LibreCat | Download (ext.)
 
[393]
2020 | Conference Paper | LibreCat-ID: 20306
Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.
LibreCat
 
[392]
2020 | Book Chapter | LibreCat-ID: 18014
El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Learning and Intelligent Optimization. LION 2020. Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:10.1007/978-3-030-53552-0_22
LibreCat | DOI
 
[391]
2020 | Preprint | LibreCat-ID: 18017
El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. arXiv:200204275.
LibreCat
 
[390]
2020 | Conference Paper | LibreCat-ID: 18276
Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020. ; 2020.
LibreCat | Download (ext.)
 
[389]
2020 | Journal Article | LibreCat-ID: 16725
Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.
LibreCat
 
[388]
2020 | Conference Paper | LibreCat-ID: 15629
Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.
LibreCat
 
[387]
2019 | Conference Abstract | LibreCat-ID: 8868
Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.
LibreCat | Files available
 
[386]
2019 | Journal Article | LibreCat-ID: 10578
Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.
LibreCat
 
[385]
2019 | Journal Article | LibreCat-ID: 15001
Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642
LibreCat | DOI
 
[384]
2019 | Journal Article | LibreCat-ID: 15002 | OA
Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324. doi:10.1007/s10618-018-0595-5
LibreCat | Files available | DOI
 
[383]
2019 | Conference Paper | LibreCat-ID: 15003
Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. 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
Ahmadi Fahandar M, Hüllermeier E. Feature Selection for Analogy-Based Learning to Rank. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_22
LibreCat | DOI
 
[381]
2019 | Book Chapter | LibreCat-ID: 15005
Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels. In: KI 2019: Advances in Artificial Intelligence. Cham; 2019. doi:10.1007/978-3-030-30179-8_3
LibreCat | DOI
 
[380]
2019 | Book Chapter | LibreCat-ID: 15006
Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_7
LibreCat | DOI
 
[379]
2019 | Conference Paper | LibreCat-ID: 15007 | OA
Melnikov V, Hüllermeier E. 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. doi:10.1016/j.jmva.2019.02.017
LibreCat | Files available | DOI
 
[378]
2019 | Conference Paper | LibreCat-ID: 15011 | OA
Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.
LibreCat | Files available
 
[377]
2019 | Conference Paper | LibreCat-ID: 15013
Brinker K, Hüllermeier E. A Reduction of Label Ranking to Multiclass Classification. In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany; 2019.
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[376]
2019 | Conference Paper | LibreCat-ID: 15014
Hüllermeier E, Couso I, Diestercke S. Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management. ; 2019.
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[375]
2019 | Journal Article | LibreCat-ID: 15015
Henzgen S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge Discovery from Data. 2019:1-36. doi:10.1145/3363572
LibreCat | DOI
 
[374]
2019 | Conference Abstract | LibreCat-ID: 13132
Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.
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[373]
2019 | Conference Paper | LibreCat-ID: 10232 | OA
Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.
LibreCat | Files available
 
[372]
2019 | Journal Article | LibreCat-ID: 20243
Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991
LibreCat | DOI
 
[371]
2018 | Conference Paper | LibreCat-ID: 2479 | OA
Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018. doi:10.1109/SCC.2018.00039
LibreCat | Files available | DOI | Download (ext.)
 
[370]
2018 | Conference Paper | LibreCat-ID: 2857 | OA
Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI; 2018:31-39.
LibreCat | Files available | Download (ext.)
 
[369]
2018 | Conference Paper | LibreCat-ID: 2471 | OA
Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036
LibreCat | Files available | DOI | Download (ext.)
 
[368]
2018 | Journal Article | LibreCat-ID: 3402
Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1
LibreCat | Files available | DOI
 
[367]
2018 | Journal Article | LibreCat-ID: 3510 | OA
Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z
LibreCat | Files available | DOI | Download (ext.)
 
[366]
2018 | Conference Paper | LibreCat-ID: 3552 | OA
Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. doi:10.1007/978-3-030-01768-2_19
LibreCat | Files available | DOI | Download (ext.)
 
[365]
2018 | Conference Paper | LibreCat-ID: 3852 | OA
Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.
LibreCat | Files available | Download (ext.)
 
[364]
2018 | Conference Paper | LibreCat-ID: 2109 | OA
Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for Classification. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM; 2018. doi:10.1145/3205455.3205562
LibreCat | Files available | DOI | Download (ext.)
 
[363]
2018 | Preprint | LibreCat-ID: 17713 | OA
Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. Published online 2018.
LibreCat | Download (ext.)
 
[362]
2018 | Preprint | LibreCat-ID: 17714 | OA
Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition. Published online 2018.
LibreCat | Download (ext.)
 
[361]
2018 | Book Chapter | LibreCat-ID: 6423
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Discovery Science. Cham: Springer International Publishing; 2018:161-175. doi:10.1007/978-3-030-01771-2_11
LibreCat | Files available | DOI
 
[360]
2018 | Conference (Editor) | LibreCat-ID: 10591
Abiteboul S, Arenas M, Barceló P, et al., eds. Research Directions for Principles of Data Management. Vol 7.; 2018:1-29.
LibreCat
 
[359]
2018 | Book Chapter | LibreCat-ID: 10783
Couso I, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Mostaghim S, Nürnberger A, Borgelt C, eds. Frontiers in Computational Intelligence. Springer; 2018:31-46.
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[358]
2018 | Journal Article | LibreCat-ID: 16038
Schäfer D, Hüllermeier E. Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning. 2018;107(5):903-941.
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[357]
2018 | Conference Paper | LibreCat-ID: 10145
Ahmadi Fahandar M, Hüllermeier E. Learning to Rank Based on Analogical Reasoning. In: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI). ; 2018:2951-2958.
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[356]
2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul A, Hüllermeier E, Busa-Fekete R. Ranking Distributions based on Noisy Sorting. In: Proc. 35th Int. Conference on Machine Learning (ICML). Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn. Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn; 2018:3469-3477.
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[355]
2018 | Conference Paper | LibreCat-ID: 10149
Hesse M, Timmermann J, Hüllermeier E, Trächtler A. 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. ; 2018:15-20.
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[354]
2018 | Book Chapter | LibreCat-ID: 10152
Mencia EL, Fürnkranz J, Hüllermeier E, Rapp M. Learning interpretable rules for multi-label classification. In: Jair Escalante H, Escalera S, Guyon I, et al., eds. Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer; 2018:81-113.
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[353]
2018 | Conference Paper | LibreCat-ID: 10181
Nguyen V-L, Destercke S, Masson M-H, Hüllermeier E. Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. In: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI). ; 2018:5089-5095.
LibreCat
 
[352]
2018 | Conference Paper | LibreCat-ID: 10184
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Proc. 21st Int. Conference on Discovery Science (DS). ; 2018:161-175.
LibreCat
 
[351]
2018 | Journal Article | LibreCat-ID: 10276
Schäfer D, Hüllermeier E. Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning. 2018;107(5):903-941.
LibreCat
 
[350]
2018 | Conference Abstract | LibreCat-ID: 1379 | OA
Seemann N, Geierhos M, Merten M-L, Tophinke D, Wever MD, Hüllermeier E. Supporting the Cognitive Process in Annotation Tasks. In: Eckart K, Schlechtweg D, eds. Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft. ; 2018.
LibreCat | Files available | Download (ext.)
 
[349]
2018 | Journal Article | LibreCat-ID: 22996
Hesse M, Timmermann J, Hüllermeier E, Trächtler A. A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Procedia Manufacturing. 2018;24:15-20.
LibreCat
 
[348]
2017 | Conference Paper | LibreCat-ID: 3325
Melnikov V, Hüllermeier E. Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In: Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing; 2017. doi:10.5445/KSP/1000074341
LibreCat | Files available | DOI
 
[347]
2017 | Conference Paper | LibreCat-ID: 71
Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Tools. In: Proceedings of the 3rd International Workshop on Software Analytics. SWAN’17. ; 2017:23-26. doi:10.1145/3121257.3121262
LibreCat | Files available | DOI
 
[346]
2017 | Report | LibreCat-ID: 72
Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Competitions.; 2017.
LibreCat | Files available
 
[345]
2017 | Encyclopedia Article | LibreCat-ID: 10589
Fürnkranz J, Hüllermeier E. Preference Learning. In: Encyclopedia of Machine Learning and Data Mining. ; 2017:1000-1005.
LibreCat
 
[344]
2017 | Book Chapter | LibreCat-ID: 10784
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Vol 107. Springer; 2017:1000-1005.
LibreCat
 
[343]
2017 | Conference Paper | LibreCat-ID: 1180 | OA
Wever MD, Mohr F, Hüllermeier E. 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
Melnikov V, Hüllermeier E. Optimizing the structure of nested dichotomies. A comparison of two heuristics. In: Hoffmann F, Hüllermeier E, Mikut R, eds. In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2017:1-12.
LibreCat
 
[341]
2017 | Conference Paper | LibreCat-ID: 15399
Czech M, Hüllermeier E, Jacobs MC, Wehrheim H. 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
Couso I, Dubois D, Hüllermeier E. Maximum likelihood estimation and coarse data. In: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain. Springer; 2017:3-16.
LibreCat
 
[339]
2017 | Conference Paper | LibreCat-ID: 10204
Ewerth R, Springstein M, Müller E, et al. Estimating relative depth in single images via rankboost. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017). ; 2017:919-924.
LibreCat
 
[338]
2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar M, Hüllermeier E, Couso I. Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In: Proc. 34th Int. Conf. on Machine Learning (ICML 2017). ; 2017:1078-1087.
LibreCat
 
[337]
2017 | Conference Paper | LibreCat-ID: 10206 | OA
Mohr F, Lettmann T, Hüllermeier E. Planning with Independent Task Networks. In: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017). ; 2017:193-206. doi:10.1007/978-3-319-67190-1_15
LibreCat | Files available | DOI
 
[336]
2017 | Conference Paper | LibreCat-ID: 10207
Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting rankings of software verification tools. In: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017. ; 2017:23-26.
LibreCat
 
[335]
2017 | Conference Paper | LibreCat-ID: 10208
Couso I, Dubois D, Hüllermeier E. Maximum Likelihood Estimation and Coarse Data. In: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017). ; 2017:3-16.
LibreCat
 
[334]
2017 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar M, Hüllermeier E. 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
Hoffmann F, Hüllermeier E, Mikut R. (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017. In: ; 2017.
LibreCat
 
[332]
2017 | Conference Paper | LibreCat-ID: 10213
Melnikov V, Hüllermeier E. Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In: Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017. ; 2017:1-12.
LibreCat
 
[331]
2017 | Conference Paper | LibreCat-ID: 10216
Shaker A, Heldt W, Hüllermeier E. 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
Bräuning M, Hüllermeier E, Keller T, Glaum M. Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting. European Journal of Operational Research. 2017;258(1):295-306.
LibreCat
 
[329]
2017 | Journal Article | LibreCat-ID: 10268
Platenius M-C, Shaker A, Becker M, Hüllermeier E, Schäfer W. Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Transactions on Software Engineering. 2017;43(8):739-759.
LibreCat
 
[328]
2017 | Journal Article | LibreCat-ID: 10269
Hüllermeier E. 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
Melnikov V, Hüllermeier E, Kaimann D, Frick B, Gupta Pritha . Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae. 2016;25. doi:10.4467/20838476si.16.006.6187
LibreCat | Files available | DOI
 
[326]
2016 | Journal Article | LibreCat-ID: 190
Platenius MC, Shaker A, Becker M, Hüllermeier E, Schäfer W. Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017. 2016;(8):739-759. doi:10.1109/TSE.2016.2632115
LibreCat | Files available | DOI
 
[325]
2016 | Conference Paper | LibreCat-ID: 184
Melnikov V, Hüllermeier E. Learning to Aggregate Using Uninorms. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016). LNCS. ; 2016:756-771. doi:10.1007/978-3-319-46227-1_47
LibreCat | Files available | DOI
 
[324]
2016 | Encyclopedia Article | LibreCat-ID: 10785
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Springer; 2016.
LibreCat
 
[323]
2016 | Conference Paper | LibreCat-ID: 15400
Labreuche C, Hüllermeier E, Vojtas P, Fallah Tehrani A. On the identifiability of models  in multi-criteria preference learning. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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
Schäfer D, Hüllermeier E. Preference -based reinforcement learning using dyad ranking. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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
Couso I, Ahmadi Fahandar M, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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
Lu S, Hüllermeier E. Support vector classification on noisy data using fuzzy superset losses. In: Hüllermeier E, Hoffmann F, Mikut R, eds. In Proceedings 26th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2016:1-8.
LibreCat
 
[319]
2016 | Conference Paper | LibreCat-ID: 15404
Schäfer D, Hüllermeier E. 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
Pfannschmidt K, Hüllermeier E, Held S, Neiger R. 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. Springer; 2016:450-461.
LibreCat
 
[317]
2016 | Journal Article | LibreCat-ID: 16041
Leinweber M, Fober T, Strickert M, et al. CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering. 2016;28(6):1423-1434.
LibreCat
 
[316]
2016 | Book Chapter | LibreCat-ID: 10214
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Springer; 2016.
LibreCat
 
[315]
2016 | Conference (Editor) | LibreCat-ID: 10221
Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.; 2016.
LibreCat
 
[314]
2016 | Conference Paper | LibreCat-ID: 10222
Jasinska K, Dembczynski K, Busa-Fekete R, Klerx T, Hüllermeier E. Extreme F-measure maximization using sparse probability estimates . In: Balcan MF, Weinberger KQ, eds. Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA. ; 2016.
LibreCat
 
[313]
2016 | Conference Paper | LibreCat-ID: 10223
Melnikov V, Hüllermeier E. 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. ; 2016:756-771.
LibreCat
 
[312]
2016 | Conference Paper | LibreCat-ID: 10224
Dembczynski K, Kotlowski W, Waegeman W, Busa-Fekete R, Hüllermeier E. 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. ; 2016:511-526.
LibreCat
 
[311]
2016 | Conference Paper | LibreCat-ID: 10225
Shabani A, Paul A, Platon R, Hüllermeier E. Predicting the electricity consumption of buildings: An improved CBR approach. In: In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA. ; 2016:356-369.
LibreCat
 
[310]
2016 | Conference Paper | LibreCat-ID: 10226
Pfannschmidt K, Hüllermeier E, Held S, Neiger R. 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. Springer; 2016:450-461.
LibreCat
 
[309]
2016 | Conference Paper | LibreCat-ID: 10227
Labreuche C, Hüllermeier E, Vojtas P, Fallah Tehrani A. On the Identifiability of models in multi-criteria preference learning . In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning. ; 2016.
LibreCat
 
[308]
2016 | Conference Paper | LibreCat-ID: 10228
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning. ; 2016.
LibreCat
 
[307]
2016 | Conference Paper | LibreCat-ID: 10229
Couso I, Ahmadi Fahandar M, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning. ; 2016.
LibreCat
 
[306]
2016 | Conference Paper | LibreCat-ID: 10230
Lu S, Hüllermeier E. Support vector classification on noisy data using fuzzy supersets losses. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing. ; 2016:1-8.
LibreCat
 
[305]
2016 | Conference Paper | LibreCat-ID: 10231
Schäfer D, Hüllermeier E. Plackett-Luce networks for dyad ranking. In: In Workshop LWDA “Lernen, Wissen, Daten, Analysen.” ; 2016.
LibreCat
 
[304]
2016 | Conference (Editor) | LibreCat-ID: 10263
Kaminka GA, Fox M, Bouquet P, 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
Leinweber M, Fober T, Strickert M, et al. CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering. 2016;28(6):1423-1434.
LibreCat
 
[302]
2016 | Journal Article | LibreCat-ID: 10266
Riemenschneider M, Senge R, Neumann U, Hüllermeier E, Heider D. Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining. 2016;9(10).
LibreCat
 
[301]
2015 | Journal Article | LibreCat-ID: 4792
Senge R, Hüllermeier E. Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033. doi:10.1109/tfuzz.2015.2396078
LibreCat | Files available | DOI
 
[300]
2015 | Conference Paper | LibreCat-ID: 15406
Schäfer D, Hüllermeier E. 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. ; 2015:110-111.
LibreCat
 
[299]
2015 | Conference Paper | LibreCat-ID: 15749
Paul A, Hüllermeier E. A cbr approach to the angry birds game. In: In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany. ; 2015:68-77.
LibreCat
 
[298]
2015 | Conference Paper | LibreCat-ID: 15750
Ewerth R, Balz A, Gehlhaar J, Dembczynski K, Hüllermeier E. Depth estimation in monocular images: Quantitative versus qualitative approaches. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany. KIT Scientific Publishing; 2015:235-240.
LibreCat
 
[297]
2015 | Conference Paper | LibreCat-ID: 15751
Lu S, Hüllermeier E. Locally weighted regression through data imprecisiation. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 25th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2015:97-104.
LibreCat
 
[296]
2015 | Journal Article | LibreCat-ID: 16049
Senge R, Hüllermeier E. Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033.
LibreCat
 
[295]
2015 | Journal Article | LibreCat-ID: 16051
Hüllermeier E. From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum. 2015;38(6):500-509.
LibreCat
 
[294]
2015 | Journal Article | LibreCat-ID: 16053
Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015;281:292-299.
LibreCat
 
[293]
2015 | Journal Article | LibreCat-ID: 16058
Waegeman W, Dembczynski K, Jachnik A, Cheng W, Hüllermeier E. On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research. 2015;15:3313-3368.
LibreCat
 
[292]
2015 | Journal Article | LibreCat-ID: 16067
Shaker A, Hüllermeier E. Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing. 2015;150:250-264.
LibreCat
 
[291]
2015 | Conference Paper | LibreCat-ID: 10234
Hüllermeier E, Minor M. Case-Based Reasoning Research and Development . In: In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer; 2015.
LibreCat
 
[290]
2015 | Conference Paper | LibreCat-ID: 10235
Hoffmann F, Hüllermeier E. Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing. In: ; 2015.
LibreCat
 
[289]
2015 | Conference Paper | LibreCat-ID: 10236
Abdel-Aziz A, Hüllermeier E. Case Base Maintenance in Preference-Based CBR. In: In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015). ; 2015:1-14.
LibreCat
 
[288]
2015 | Conference Paper | LibreCat-ID: 10237
Szörényi B, Busa-Fekete R, Weng P, Hüllermeier E. Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In: In Proceedings International Conference on Machine Learning (ICML 2015). ; 2015:1660-1668.
LibreCat
 
[287]
2015 | Conference Paper | LibreCat-ID: 10238
Schäfer D, Hüllermeier E. Dyad Ranking Using A Bilinear Plackett-Luce Model. In: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD). ; 2015:227-242.
LibreCat
 
[286]
2015 | Conference Paper | LibreCat-ID: 10239
Hüllermeier E, Cheng W. Superset Learning Based on Generalized Loss Minimization . In: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD). ; 2015:260-275.
LibreCat
 
[285]
2015 | Conference Paper | LibreCat-ID: 10240
Henzgen S, Hüllermeier E. 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). ; 2015:422-437.
LibreCat
 
[284]
2015 | Conference Paper | LibreCat-ID: 10241
Szörényi B, Busa-Fekete R, Paul A, Hüllermeier E. Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In: In Advances in Neural Information Processing Systems 28 (NIPS 2015). ; 2015:604-612.
LibreCat
 
[283]
2015 | Conference Paper | LibreCat-ID: 10242
Szörényi B, Busa-Fekete R, Dembczynski K, Hüllermeier E. Online F-Measure Optimization. In: In Advances in Neural Information Processing Systems 28 (NIPS 2015). ; 2015:595-603.
LibreCat
 
[282]
2015 | Conference Paper | LibreCat-ID: 10243
El Mesaoudi-Paul A, Hüllermeier E. A CBR Approach to the Angry Birds Game. In: In Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015). ; 2015:68-77.
LibreCat
 
[281]
2015 | Conference Paper | LibreCat-ID: 10244
Schäfer D, Hüllermeier E. 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). ; 2015:110-111.
LibreCat
 
[280]
2015 | Conference Paper | LibreCat-ID: 10245
Lu S, Hüllermeier E. Locally weighted regression through data imprecisiation. In: Proceedings 25. Workshop Computational Intelligence. ; 2015:97-104.
LibreCat
 
[279]
2015 | Conference Paper | LibreCat-ID: 10246
Ewerth R, Balz A, Gehlhaar J, Dembczynski K, Hüllermeier E. Depth estimation in monocular images: Quantitative versus qualitative approaches. In: Proceedings 25. Workshop Computational Intelligence. ; 2015:235-240.
LibreCat
 
[278]
2015 | Journal Article | LibreCat-ID: 10319
Waegeman W, Dembczynski K, Jachnik A, Cheng W, Hüllermeier E. On the Bayes-Optimality of F-Measure Maximizers. in Journal of Machine Learning Research. 2015;15:3333-3388.
LibreCat
 
[277]
2015 | Journal Article | LibreCat-ID: 10320
Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015;281:292-299.
LibreCat
 
[276]
2015 | Journal Article | LibreCat-ID: 10321
Shaker A, Hüllermeier E. Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing. 2015;150:250-264.
LibreCat
 
[275]
2015 | Journal Article | LibreCat-ID: 10322
Hüllermeier E. From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum. 2015;38(6):500-509.
LibreCat
 
[274]
2015 | Journal Article | LibreCat-ID: 10323
Garcia-Jimenez S, Bustince U, Hüllermeier E, Mesiar R, Pal NR, Pradera A. Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems. IEEE Transactions on Fuzzy Systems. 2015;23(4):1259-1273.
LibreCat
 
[273]
2015 | Journal Article | LibreCat-ID: 10324
Senge R, Hüllermeier E. Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033.
LibreCat
 
[272]
2014 | Journal Article | LibreCat-ID: 16046
Agarwal M, Fallah Tehrani A, Hüllermeier E. Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis. 2014;22(3-4).
LibreCat
 
[271]
2014 | Journal Article | LibreCat-ID: 16060
Krotzky T, Fober T, Hüllermeier E, Klebe G. Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics. 2014;11(5):878-890.
LibreCat
 
[270]
2014 | Journal Article | LibreCat-ID: 16064
Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning. 2014;55(7):1519-1534.
LibreCat
 
[269]
2014 | Journal Article | LibreCat-ID: 16069
Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy-rule-based systems. Evolving Systems. 2014;5:175-191.
LibreCat
 
[268]
2014 | Journal Article | LibreCat-ID: 16077
Busa-Fekete R, Szörenyi B, Weng P, Cheng W, Hüllermeier E. Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning. 2014;97(3):327-351.
LibreCat
 
[267]
2014 | Journal Article | LibreCat-ID: 16078
Krempl G, Zliobaite I, Brzezinski D, et al. Open challenges for data stream mining research. SIGKDD Explorations. 2014;16(1):1-10.
LibreCat
 
[266]
2014 | Journal Article | LibreCat-ID: 16079
Strickert M, Bunte K, Schleif FM, Hüllermeier E. Correlation-based embedding of pairwise score data. Neurocomputing. 2014;141:97-109.
LibreCat
 
[265]
2014 | Journal Article | LibreCat-ID: 16080
Shaker A, Hüllermeier E. Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science. 2014;24(1):199-212.
LibreCat
 
[264]
2014 | Journal Article | LibreCat-ID: 16082
Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences. 2014;255:16-29.
LibreCat
 
[263]
2014 | Journal Article | LibreCat-ID: 16083
Donner-Banzhoff N, Haasenritter J, Hüllermeier E, Viniol A, Bösner S, Becker A. The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology. 2014;2(67):124-132.
LibreCat
 
[262]
2014 | Conference Paper | LibreCat-ID: 10247
Busa-Fekete R, Szörényi B, Hüllermeier E. PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In: Proceedings AAAI 2014, Quebec, Canada. ; 2014:1701-1707.
LibreCat
 
[261]
2014 | Conference Paper | LibreCat-ID: 10248
Busa-Fekete R, Hüllermeier E. A Survey of Preference-Based Online Learning with Bandit Algorithms. In: Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia. ; 2014:18-39.
LibreCat
 
[260]
2014 | Conference Paper | LibreCat-ID: 10249
Henzgen S, Hüllermeier E. Mining Rank Data. In: Proceedings Discovery Science, Bled,Slovenia . ; 2014:123-134.
LibreCat
 
[259]
2014 | Conference Paper | LibreCat-ID: 10250
Fallah Tehrani A, Strickert M, Hüllermeier E. The Choquet kernel for monotone data. In: Proceedings ESANN , Bruges, Belgium. ; 2014.
LibreCat
 
[258]
2014 | Conference Paper | LibreCat-ID: 10251
Abdel-Aziz A, Strickert M, Hüllermeier E. Learning Solution Similarity in Preference-Based CBR. In: Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland. ; 2014:17-31.
LibreCat
 
[257]
2014 | Conference Paper | LibreCat-ID: 10253
Schäfer D, Hüllermeier E. Dyad Ranking Using A Bilinear Plackett-Luce Model. In: Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany. ; 2014:32-33.
LibreCat
 
[256]
2014 | Conference Paper | LibreCat-ID: 10254
Calders T, Esposito F, Hüllermeier E, Meo R. Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In: Proceedings, Parts I-III. Lecture Notes in Computer Science. Springer; 2014:8724-8726.
LibreCat
 
[255]
2014 | Conference Paper | LibreCat-ID: 10295
Fürnkranz J, Hüllermeier E, Rudin C, Slowinski R, Sanner S. Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports. In: Vol 4. ; 2014:1-27.
LibreCat
 
[254]
2014 | Journal Article | LibreCat-ID: 10296
Shaker A, Hüllermeier E. Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Applied Mathematics and Computer Science. 2014;24(1):199-212.
LibreCat
 
[253]
2014 | Journal Article | LibreCat-ID: 10297
Hoffmann F, Hüllermeier E, Kroll A. Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik. 2014;62(10):685-686.
LibreCat
 
[252]
2014 | Journal Article | LibreCat-ID: 10298
Calders T, Esposito F, Hüllermeier E, Meo R. Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Data Min Knowledge Discovery. 2014;28(5-6):1129-1133.
LibreCat
 
[251]
2014 | Journal Article | LibreCat-ID: 10299
Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy rule-based systems. Evolving Systems. 2014;5(3):175-191.
LibreCat
 
[250]
2014 | Journal Article | LibreCat-ID: 10308
Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int J Approx Reasoning. 2014;55(7):1519-1534.
LibreCat
 
[249]
2014 | Journal Article | LibreCat-ID: 10309
Hüllermeier E. Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int J Approx Reasoning. 2014;55(7):1609-1613.
LibreCat
 
[248]
2014 | Journal Article | LibreCat-ID: 10310
Strickert M, Bunte K, Schleif F-M, Hüllermeier E. Correlation-based embedding of pairwise score data. Neurocomputing. 2014;141:97-109.
LibreCat
 
[247]
2014 | Journal Article | LibreCat-ID: 10311
Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences. 2014;255:16-29.
LibreCat
 
[246]
2014 | Journal Article | LibreCat-ID: 10312
Mernberger M, Moog M, Stork S, Zauner S, Maier UG, Hüllermeier E. Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J Bioinformatics and Computational Biology. 2014;12(1).
LibreCat
 
[245]
2014 | Journal Article | LibreCat-ID: 10313
Calders T, Esposito F, Hüllermeier E, Meo R. Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Machine Learning. 2014;97(1-2):1-3.
LibreCat
 
[244]
2014 | Journal Article | LibreCat-ID: 10314
Busa-Fekete R, Szörényi B, Weng P, Cheng W, Hüllermeier E. Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning. 2014;97(3):327-351.
LibreCat
 
[243]
2014 | Journal Article | LibreCat-ID: 10315
Montanés E, Senge R, Barranquero J, Quevedo JR, Del Coz JJ, Hüllermeier E. Dependent binary relevance models for multi-label classification. Pattern Recognition. 2014;47(3):1494-1508.
LibreCat
 
[242]
2014 | Journal Article | LibreCat-ID: 10316
Krempl G, Zliobaite I, Brzezinski D, et al. Open challenges for data stream mining research. SIGKDD Explorations. 2014;16(1):1-10.
LibreCat
 
[241]
2014 | Journal Article | LibreCat-ID: 10317
Krotzky T, Fober T, Hüllermeier E, Klebe G. Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Trans Comput Biology Bioinform. 2014;11(5):878-890.
LibreCat
 
[240]
2014 | Journal Article | LibreCat-ID: 10318
Stock M, Fober T, Hüllermeier E, et al. Identification of Functionally Releated Enzymes by Learning to Rank Methods. IEEE/ACM Trans Comput Biology Bioinform. 2014;11(6):1157-1169.
LibreCat
 
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[439]
2024 | Journal Article | LibreCat-ID: 53073
Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence. 2024;38(13):14388-14396. doi:10.1609/aaai.v38i13.29352
LibreCat | DOI
 
[438]
2023 | Preprint | LibreCat-ID: 44512 | OA
Uhlemeyer S, Lienen J, Hüllermeier E, Gottschalk H. Detecting Novelties with Empty Classes. arXiv:230500983. Published online 2023.
LibreCat | Download (ext.) | arXiv
 
[437]
2023 | Conference Paper | LibreCat-ID: 31880 | OA
Nguyen DA, Levie R, Lienen J, Kutyniok G, Hüllermeier E. Memorization-Dilation: Modeling Neural Collapse Under Noise. In: International Conference on Learning Representations, ICLR. ; 2023.
LibreCat | Download (ext.)
 
[436]
2023 | Book Chapter | LibreCat-ID: 45884 | OA
Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104. doi:10.5281/zenodo.8068466
LibreCat | Files available | DOI
 
[435]
2023 | Book Chapter | LibreCat-ID: 45886 | OA
Wehrheim H, Hüllermeier E, Becker S, Becker M, Richter C, Sharma A. Composition Analysis in Unknown Contexts. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:105-123. doi:10.5281/zenodo.8068510
LibreCat | Files available | DOI
 
[434]
2023 | Preprint | LibreCat-ID: 45911 | OA
Lienen J, Hüllermeier E. Mitigating Label Noise through Data Ambiguation. arXiv:230513764. Published online 2023.
LibreCat | Download (ext.) | arXiv
 
[433]
2023 | Journal Article | LibreCat-ID: 21600
Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing. 2023;45(2):A579-A595. doi:10.1137/21M1412682
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[432]
2023 | Conference Paper | LibreCat-ID: 51373
Hanselle JM, Fürnkranz J, Hüllermeier E. Probabilistic Scoring Lists for Interpretable Machine Learning. In: 26th International Conference on Discovery Science . Vol 14050. Lecture Notes in Computer Science. Springer Nature Switzerland; 2023:189-203. doi:10.1007/978-3-031-45275-8_13
LibreCat | DOI
 
[431]
2023 | Book Chapter | LibreCat-ID: 48776
Muschalik M, Fumagalli F, Hammer B, Huellermeier E. iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In: Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-43418-1_26
LibreCat | DOI
 
[430]
2023 | Book Chapter | LibreCat-ID: 48778
Muschalik M, Fumagalli F, Jagtani R, Hammer B, Huellermeier E. iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In: Communications in Computer and Information Science. Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-44064-9_11
LibreCat | DOI
 
[429]
2023 | Conference Paper | LibreCat-ID: 48775
Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. On Feature Removal for Explainability in Dynamic Environments. In: ESANN 2023 Proceedings. i6doc.com publ.; 2023. doi:10.14428/esann/2023.es2023-148
LibreCat | DOI
 
[428]
2023 | Conference Paper | LibreCat-ID: 52230
Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. SHAP-IQ: Unified Approximation of any-order Shapley Interactions. In: NeurIPS 2023 - Advances in Neural Information Processing Systems. Vol 36. Curran Associates, Inc.; 2023:11515--11551.
LibreCat
 
[427]
2022 | Preprint | LibreCat-ID: 30868
Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. arXiv:220201651. Published online 2022.
LibreCat | arXiv
 
[426]
2022 | Conference Paper | LibreCat-ID: 32311
Sharma A, Melnikov V, Hüllermeier E, Wehrheim H. Property-Driven Testing of Black-Box Functions. In: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE). IEEE; 2022:113-123.
LibreCat
 
[425]
2022 | Conference Paper | LibreCat-ID: 34542
Campagner A, Lienen J, Hüllermeier E, Ciucci D. Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. In: Lecture Notes in Computer Science. Vol 13633. Springer; 2022:57-70.
LibreCat
 
[424]
2022 | Preprint | LibreCat-ID: 31546 | OA
Lienen J, Demir C, Hüllermeier E. Conformal Credal Self-Supervised Learning. arXiv:220515239. Published online 2022.
LibreCat | Download (ext.)
 
[423]
2022 | Preprint | LibreCat-ID: 30867
Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. Proceedings of the 36th AAAI Conference on Artificial Intelligence. Published online 2022.
LibreCat | arXiv
 
[422]
2022 | Preprint | LibreCat-ID: 30865
Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. Machine Learning. Published online 2022.
LibreCat | arXiv
 
[421]
2022 | Journal Article | LibreCat-ID: 33090
Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. Published online 2022. doi:10.1007/s40194-022-01339-9
LibreCat | DOI
 
[420]
2022 | Report | LibreCat-ID: 36227
Hammer B, Hüllermeier E, Lohweg V, et al. Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens.; 2022. doi:10.4119/unibi/2965622
LibreCat | DOI
 
[419]
2022 | Journal Article | LibreCat-ID: 48780
Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz. 2022;36(3-4):211-224. doi:10.1007/s13218-022-00766-6
LibreCat | DOI
 
[418]
2021 | Journal Article | LibreCat-ID: 24143
Drees JP, Gupta P, Hüllermeier E, et al. Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! 14th ACM Workshop on Artificial Intelligence and Security. Published online 2021.
LibreCat
 
[417]
2021 | Journal Article | LibreCat-ID: 24148
Ramaswamy A, Hüllermeier E. Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. IEEE Transactions on Artificial Intelligence (to appear). Published online 2021.
LibreCat
 
[416]
2021 | Journal Article | LibreCat-ID: 21004
Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276
LibreCat | DOI
 
[415]
2021 | Journal Article | LibreCat-ID: 21092
Mohr F, Wever MD, Tornede A, Hüllermeier E. 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
Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings of the Genetic and Evolutionary Computation Conference. ; 2021.
LibreCat
 
[413]
2021 | Journal Article | LibreCat-ID: 21636
Lienen J, Hüllermeier E. Instance weighting through data imprecisiation. International Journal of Approximate Reasoning. 2021.
LibreCat | Download (ext.)
 
[412]
2021 | Conference Paper | LibreCat-ID: 21637 | OA
Lienen J, Hüllermeier E. From Label Smoothing to Label Relaxation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI. Vol 35. AAAI Press; 2021:8583-8591.
LibreCat | Download (ext.)
 
[411]
2021 | Conference Paper | LibreCat-ID: 23779
Bernijazov R, Dicks A, Dumitrescu R, et al. A Meta-Review on Artificial Intelligence in Product Creation. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). ; 2021.
LibreCat | Download (ext.)
 
[410]
2021 | Conference Paper | LibreCat-ID: 22280
Lienen J, Hüllermeier E, Ewerth R, Nommensen N. 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. ; 2021:14595-14604.
LibreCat
 
[409]
2021 | Preprint | LibreCat-ID: 22509 | OA
Lienen J, Hüllermeier E. Credal Self-Supervised Learning. arXiv:210611853. 2021.
LibreCat | Download (ext.)
 
[408]
2021 | Conference Paper | LibreCat-ID: 22913
Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021.
LibreCat
 
[407]
2021 | Conference Paper | LibreCat-ID: 27381
Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5
LibreCat | DOI | arXiv
 
[406]
2021 | Preprint | LibreCat-ID: 30866
Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850. Published online 2021.
LibreCat | arXiv
 
[405]
2021 | Conference Paper | LibreCat-ID: 21198
Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.
LibreCat
 
[404]
2021 | Book Chapter | LibreCat-ID: 29292 | OA
Feldhans R, Wilke A, Heindorf S, et al. Drift Detection in Text Data with Document Embeddings. In: Intelligent Data Engineering and Automated Learning – IDEAL 2021. Springer International Publishing; 2021. doi:10.1007/978-3-030-91608-4_11
LibreCat | Files available | DOI | Download (ext.)
 
[403]
2021 | Working Paper | LibreCat-ID: 45616
van Straaten D, Melnikov V, Hüllermeier E, Mir Djawadi B, Fahr R. Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes. Vol 72.; 2021.
LibreCat
 
[402]
2021 | Journal Article | LibreCat-ID: 24456 | OA
Rohlfing KJ, Cimiano P, Scharlau I, 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. 2021;13(3):717-728. doi:10.1109/tcds.2020.3044366
LibreCat | Files available | DOI
 
[401]
2020 | Preprint | LibreCat-ID: 19603 | OA
Bode H, Heid SH, Weber D, Hüllermeier E, Wallscheid O. Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control. arXiv:200504869. 2020.
LibreCat | Download (ext.)
 
[400]
2020 | Conference Paper | LibreCat-ID: 19953 | OA
Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.
LibreCat | Files available | arXiv
 
[399]
2020 | Preprint | LibreCat-ID: 20211 | OA
Lienen J, Hüllermeier E. Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model. arXiv:201013118. 2020.
LibreCat | Download (ext.)
 
[398]
2020 | Conference Paper | LibreCat-ID: 24146
Heid SH, Ramaswamy A, Hüllermeier E. Constrained Multi-Agent Optimization with Unbounded Information Delay. In: Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020. Vol 26. ; 2020:247.
LibreCat
 
[397]
2020 | Conference Paper | LibreCat-ID: 17407
Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: Discovery Science. ; 2020.
LibreCat
 
[396]
2020 | Conference Paper | LibreCat-ID: 17408
Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence. ; 2020.
LibreCat
 
[395]
2020 | Conference Paper | LibreCat-ID: 17424
Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020. ; 2020. doi:10.1007/978-3-030-66770-2_8
LibreCat | DOI
 
[394]
2020 | Preprint | LibreCat-ID: 17605 | OA
Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.
LibreCat | Download (ext.)
 
[393]
2020 | Conference Paper | LibreCat-ID: 20306
Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.
LibreCat
 
[392]
2020 | Book Chapter | LibreCat-ID: 18014
El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Learning and Intelligent Optimization. LION 2020. Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:10.1007/978-3-030-53552-0_22
LibreCat | DOI
 
[391]
2020 | Preprint | LibreCat-ID: 18017
El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. arXiv:200204275.
LibreCat
 
[390]
2020 | Conference Paper | LibreCat-ID: 18276
Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020. ; 2020.
LibreCat | Download (ext.)
 
[389]
2020 | Journal Article | LibreCat-ID: 16725
Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.
LibreCat
 
[388]
2020 | Conference Paper | LibreCat-ID: 15629
Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.
LibreCat
 
[387]
2019 | Conference Abstract | LibreCat-ID: 8868
Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.
LibreCat | Files available
 
[386]
2019 | Journal Article | LibreCat-ID: 10578
Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.
LibreCat
 
[385]
2019 | Journal Article | LibreCat-ID: 15001
Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642
LibreCat | DOI
 
[384]
2019 | Journal Article | LibreCat-ID: 15002 | OA
Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324. doi:10.1007/s10618-018-0595-5
LibreCat | Files available | DOI
 
[383]
2019 | Conference Paper | LibreCat-ID: 15003
Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. 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
Ahmadi Fahandar M, Hüllermeier E. Feature Selection for Analogy-Based Learning to Rank. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_22
LibreCat | DOI
 
[381]
2019 | Book Chapter | LibreCat-ID: 15005
Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels. In: KI 2019: Advances in Artificial Intelligence. Cham; 2019. doi:10.1007/978-3-030-30179-8_3
LibreCat | DOI
 
[380]
2019 | Book Chapter | LibreCat-ID: 15006
Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_7
LibreCat | DOI
 
[379]
2019 | Conference Paper | LibreCat-ID: 15007 | OA
Melnikov V, Hüllermeier E. 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. doi:10.1016/j.jmva.2019.02.017
LibreCat | Files available | DOI
 
[378]
2019 | Conference Paper | LibreCat-ID: 15011 | OA
Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.
LibreCat | Files available
 
[377]
2019 | Conference Paper | LibreCat-ID: 15013
Brinker K, Hüllermeier E. A Reduction of Label Ranking to Multiclass Classification. In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany; 2019.
LibreCat
 
[376]
2019 | Conference Paper | LibreCat-ID: 15014
Hüllermeier E, Couso I, Diestercke S. 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
Henzgen S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge Discovery from Data. 2019:1-36. doi:10.1145/3363572
LibreCat | DOI
 
[374]
2019 | Conference Abstract | LibreCat-ID: 13132
Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.
LibreCat
 
[373]
2019 | Conference Paper | LibreCat-ID: 10232 | OA
Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.
LibreCat | Files available
 
[372]
2019 | Journal Article | LibreCat-ID: 20243
Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991
LibreCat | DOI
 
[371]
2018 | Conference Paper | LibreCat-ID: 2479 | OA
Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018. doi:10.1109/SCC.2018.00039
LibreCat | Files available | DOI | Download (ext.)
 
[370]
2018 | Conference Paper | LibreCat-ID: 2857 | OA
Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI; 2018:31-39.
LibreCat | Files available | Download (ext.)
 
[369]
2018 | Conference Paper | LibreCat-ID: 2471 | OA
Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036
LibreCat | Files available | DOI | Download (ext.)
 
[368]
2018 | Journal Article | LibreCat-ID: 3402
Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1
LibreCat | Files available | DOI
 
[367]
2018 | Journal Article | LibreCat-ID: 3510 | OA
Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z
LibreCat | Files available | DOI | Download (ext.)
 
[366]
2018 | Conference Paper | LibreCat-ID: 3552 | OA
Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. doi:10.1007/978-3-030-01768-2_19
LibreCat | Files available | DOI | Download (ext.)
 
[365]
2018 | Conference Paper | LibreCat-ID: 3852 | OA
Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.
LibreCat | Files available | Download (ext.)
 
[364]
2018 | Conference Paper | LibreCat-ID: 2109 | OA
Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for Classification. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM; 2018. doi:10.1145/3205455.3205562
LibreCat | Files available | DOI | Download (ext.)
 
[363]
2018 | Preprint | LibreCat-ID: 17713 | OA
Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. Published online 2018.
LibreCat | Download (ext.)
 
[362]
2018 | Preprint | LibreCat-ID: 17714 | OA
Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition. Published online 2018.
LibreCat | Download (ext.)
 
[361]
2018 | Book Chapter | LibreCat-ID: 6423
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Discovery Science. Cham: Springer International Publishing; 2018:161-175. doi:10.1007/978-3-030-01771-2_11
LibreCat | Files available | DOI
 
[360]
2018 | Conference (Editor) | LibreCat-ID: 10591
Abiteboul S, Arenas M, Barceló P, et al., eds. Research Directions for Principles of Data Management. Vol 7.; 2018:1-29.
LibreCat
 
[359]
2018 | Book Chapter | LibreCat-ID: 10783
Couso I, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Mostaghim S, Nürnberger A, Borgelt C, eds. Frontiers in Computational Intelligence. Springer; 2018:31-46.
LibreCat
 
[358]
2018 | Journal Article | LibreCat-ID: 16038
Schäfer D, Hüllermeier E. Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning. 2018;107(5):903-941.
LibreCat
 
[357]
2018 | Conference Paper | LibreCat-ID: 10145
Ahmadi Fahandar M, Hüllermeier E. Learning to Rank Based on Analogical Reasoning. In: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI). ; 2018:2951-2958.
LibreCat
 
[356]
2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul A, Hüllermeier E, Busa-Fekete R. Ranking Distributions based on Noisy Sorting. In: Proc. 35th Int. Conference on Machine Learning (ICML). Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn. Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn; 2018:3469-3477.
LibreCat
 
[355]
2018 | Conference Paper | LibreCat-ID: 10149
Hesse M, Timmermann J, Hüllermeier E, Trächtler A. 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. ; 2018:15-20.
LibreCat
 
[354]
2018 | Book Chapter | LibreCat-ID: 10152
Mencia EL, Fürnkranz J, Hüllermeier E, Rapp M. Learning interpretable rules for multi-label classification. In: Jair Escalante H, Escalera S, Guyon I, et al., eds. Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer; 2018:81-113.
LibreCat
 
[353]
2018 | Conference Paper | LibreCat-ID: 10181
Nguyen V-L, Destercke S, Masson M-H, Hüllermeier E. Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. In: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI). ; 2018:5089-5095.
LibreCat
 
[352]
2018 | Conference Paper | LibreCat-ID: 10184
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Proc. 21st Int. Conference on Discovery Science (DS). ; 2018:161-175.
LibreCat
 
[351]
2018 | Journal Article | LibreCat-ID: 10276
Schäfer D, Hüllermeier E. Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning. 2018;107(5):903-941.
LibreCat
 
[350]
2018 | Conference Abstract | LibreCat-ID: 1379 | OA
Seemann N, Geierhos M, Merten M-L, Tophinke D, Wever MD, Hüllermeier E. Supporting the Cognitive Process in Annotation Tasks. In: Eckart K, Schlechtweg D, eds. Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft. ; 2018.
LibreCat | Files available | Download (ext.)
 
[349]
2018 | Journal Article | LibreCat-ID: 22996
Hesse M, Timmermann J, Hüllermeier E, Trächtler A. A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Procedia Manufacturing. 2018;24:15-20.
LibreCat
 
[348]
2017 | Conference Paper | LibreCat-ID: 3325
Melnikov V, Hüllermeier E. Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In: Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing; 2017. doi:10.5445/KSP/1000074341
LibreCat | Files available | DOI
 
[347]
2017 | Conference Paper | LibreCat-ID: 71
Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Tools. In: Proceedings of the 3rd International Workshop on Software Analytics. SWAN’17. ; 2017:23-26. doi:10.1145/3121257.3121262
LibreCat | Files available | DOI
 
[346]
2017 | Report | LibreCat-ID: 72
Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Competitions.; 2017.
LibreCat | Files available
 
[345]
2017 | Encyclopedia Article | LibreCat-ID: 10589
Fürnkranz J, Hüllermeier E. Preference Learning. In: Encyclopedia of Machine Learning and Data Mining. ; 2017:1000-1005.
LibreCat
 
[344]
2017 | Book Chapter | LibreCat-ID: 10784
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Vol 107. Springer; 2017:1000-1005.
LibreCat
 
[343]
2017 | Conference Paper | LibreCat-ID: 1180 | OA
Wever MD, Mohr F, Hüllermeier E. 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
Melnikov V, Hüllermeier E. Optimizing the structure of nested dichotomies. A comparison of two heuristics. In: Hoffmann F, Hüllermeier E, Mikut R, eds. In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2017:1-12.
LibreCat
 
[341]
2017 | Conference Paper | LibreCat-ID: 15399
Czech M, Hüllermeier E, Jacobs MC, Wehrheim H. 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
Couso I, Dubois D, Hüllermeier E. Maximum likelihood estimation and coarse data. In: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain. Springer; 2017:3-16.
LibreCat
 
[339]
2017 | Conference Paper | LibreCat-ID: 10204
Ewerth R, Springstein M, Müller E, et al. Estimating relative depth in single images via rankboost. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017). ; 2017:919-924.
LibreCat
 
[338]
2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar M, Hüllermeier E, Couso I. Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In: Proc. 34th Int. Conf. on Machine Learning (ICML 2017). ; 2017:1078-1087.
LibreCat
 
[337]
2017 | Conference Paper | LibreCat-ID: 10206 | OA
Mohr F, Lettmann T, Hüllermeier E. Planning with Independent Task Networks. In: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017). ; 2017:193-206. doi:10.1007/978-3-319-67190-1_15
LibreCat | Files available | DOI
 
[336]
2017 | Conference Paper | LibreCat-ID: 10207
Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting rankings of software verification tools. In: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017. ; 2017:23-26.
LibreCat
 
[335]
2017 | Conference Paper | LibreCat-ID: 10208
Couso I, Dubois D, Hüllermeier E. Maximum Likelihood Estimation and Coarse Data. In: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017). ; 2017:3-16.
LibreCat
 
[334]
2017 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar M, Hüllermeier E. 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
Hoffmann F, Hüllermeier E, Mikut R. (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017. In: ; 2017.
LibreCat
 
[332]
2017 | Conference Paper | LibreCat-ID: 10213
Melnikov V, Hüllermeier E. Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In: Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017. ; 2017:1-12.
LibreCat
 
[331]
2017 | Conference Paper | LibreCat-ID: 10216
Shaker A, Heldt W, Hüllermeier E. 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
Bräuning M, Hüllermeier E, Keller T, Glaum M. Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting. European Journal of Operational Research. 2017;258(1):295-306.
LibreCat
 
[329]
2017 | Journal Article | LibreCat-ID: 10268
Platenius M-C, Shaker A, Becker M, Hüllermeier E, Schäfer W. Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Transactions on Software Engineering. 2017;43(8):739-759.
LibreCat
 
[328]
2017 | Journal Article | LibreCat-ID: 10269
Hüllermeier E. 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
Melnikov V, Hüllermeier E, Kaimann D, Frick B, Gupta Pritha . Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae. 2016;25. doi:10.4467/20838476si.16.006.6187
LibreCat | Files available | DOI
 
[326]
2016 | Journal Article | LibreCat-ID: 190
Platenius MC, Shaker A, Becker M, Hüllermeier E, Schäfer W. Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017. 2016;(8):739-759. doi:10.1109/TSE.2016.2632115
LibreCat | Files available | DOI
 
[325]
2016 | Conference Paper | LibreCat-ID: 184
Melnikov V, Hüllermeier E. Learning to Aggregate Using Uninorms. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016). LNCS. ; 2016:756-771. doi:10.1007/978-3-319-46227-1_47
LibreCat | Files available | DOI
 
[324]
2016 | Encyclopedia Article | LibreCat-ID: 10785
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Springer; 2016.
LibreCat
 
[323]
2016 | Conference Paper | LibreCat-ID: 15400
Labreuche C, Hüllermeier E, Vojtas P, Fallah Tehrani A. On the identifiability of models  in multi-criteria preference learning. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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
Schäfer D, Hüllermeier E. Preference -based reinforcement learning using dyad ranking. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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
Couso I, Ahmadi Fahandar M, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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
Lu S, Hüllermeier E. Support vector classification on noisy data using fuzzy superset losses. In: Hüllermeier E, Hoffmann F, Mikut R, eds. In Proceedings 26th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2016:1-8.
LibreCat
 
[319]
2016 | Conference Paper | LibreCat-ID: 15404
Schäfer D, Hüllermeier E. 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
Pfannschmidt K, Hüllermeier E, Held S, Neiger R. 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. Springer; 2016:450-461.
LibreCat
 
[317]
2016 | Journal Article | LibreCat-ID: 16041
Leinweber M, Fober T, Strickert M, et al. CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering. 2016;28(6):1423-1434.
LibreCat
 
[316]
2016 | Book Chapter | LibreCat-ID: 10214
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Springer; 2016.
LibreCat
 
[315]
2016 | Conference (Editor) | LibreCat-ID: 10221
Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.; 2016.
LibreCat
 
[314]
2016 | Conference Paper | LibreCat-ID: 10222
Jasinska K, Dembczynski K, Busa-Fekete R, Klerx T, Hüllermeier E. Extreme F-measure maximization using sparse probability estimates . In: Balcan MF, Weinberger KQ, eds. Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA. ; 2016.
LibreCat
 
[313]
2016 | Conference Paper | LibreCat-ID: 10223
Melnikov V, Hüllermeier E. 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. ; 2016:756-771.
LibreCat
 
[312]
2016 | Conference Paper | LibreCat-ID: 10224
Dembczynski K, Kotlowski W, Waegeman W, Busa-Fekete R, Hüllermeier E. 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. ; 2016:511-526.
LibreCat
 
[311]
2016 | Conference Paper | LibreCat-ID: 10225
Shabani A, Paul A, Platon R, Hüllermeier E. Predicting the electricity consumption of buildings: An improved CBR approach. In: In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA. ; 2016:356-369.
LibreCat
 
[310]
2016 | Conference Paper | LibreCat-ID: 10226
Pfannschmidt K, Hüllermeier E, Held S, Neiger R. 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. Springer; 2016:450-461.
LibreCat
 
[309]
2016 | Conference Paper | LibreCat-ID: 10227
Labreuche C, Hüllermeier E, Vojtas P, Fallah Tehrani A. On the Identifiability of models in multi-criteria preference learning . In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning. ; 2016.
LibreCat
 
[308]
2016 | Conference Paper | LibreCat-ID: 10228
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning. ; 2016.
LibreCat
 
[307]
2016 | Conference Paper | LibreCat-ID: 10229
Couso I, Ahmadi Fahandar M, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning. ; 2016.
LibreCat
 
[306]
2016 | Conference Paper | LibreCat-ID: 10230
Lu S, Hüllermeier E. Support vector classification on noisy data using fuzzy supersets losses. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing. ; 2016:1-8.
LibreCat
 
[305]
2016 | Conference Paper | LibreCat-ID: 10231
Schäfer D, Hüllermeier E. Plackett-Luce networks for dyad ranking. In: In Workshop LWDA “Lernen, Wissen, Daten, Analysen.” ; 2016.
LibreCat
 
[304]
2016 | Conference (Editor) | LibreCat-ID: 10263
Kaminka GA, Fox M, Bouquet P, 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
Leinweber M, Fober T, Strickert M, et al. CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering. 2016;28(6):1423-1434.
LibreCat
 
[302]
2016 | Journal Article | LibreCat-ID: 10266
Riemenschneider M, Senge R, Neumann U, Hüllermeier E, Heider D. Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining. 2016;9(10).
LibreCat
 
[301]
2015 | Journal Article | LibreCat-ID: 4792
Senge R, Hüllermeier E. Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033. doi:10.1109/tfuzz.2015.2396078
LibreCat | Files available | DOI
 
[300]
2015 | Conference Paper | LibreCat-ID: 15406
Schäfer D, Hüllermeier E. 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. ; 2015:110-111.
LibreCat
 
[299]
2015 | Conference Paper | LibreCat-ID: 15749
Paul A, Hüllermeier E. A cbr approach to the angry birds game. In: In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany. ; 2015:68-77.
LibreCat
 
[298]
2015 | Conference Paper | LibreCat-ID: 15750
Ewerth R, Balz A, Gehlhaar J, Dembczynski K, Hüllermeier E. Depth estimation in monocular images: Quantitative versus qualitative approaches. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany. KIT Scientific Publishing; 2015:235-240.
LibreCat
 
[297]
2015 | Conference Paper | LibreCat-ID: 15751
Lu S, Hüllermeier E. Locally weighted regression through data imprecisiation. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 25th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2015:97-104.
LibreCat
 
[296]
2015 | Journal Article | LibreCat-ID: 16049
Senge R, Hüllermeier E. Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033.
LibreCat
 
[295]
2015 | Journal Article | LibreCat-ID: 16051
Hüllermeier E. From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum. 2015;38(6):500-509.
LibreCat
 
[294]
2015 | Journal Article | LibreCat-ID: 16053
Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015;281:292-299.
LibreCat
 
[293]
2015 | Journal Article | LibreCat-ID: 16058
Waegeman W, Dembczynski K, Jachnik A, Cheng W, Hüllermeier E. On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research. 2015;15:3313-3368.
LibreCat
 
[292]
2015 | Journal Article | LibreCat-ID: 16067
Shaker A, Hüllermeier E. Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing. 2015;150:250-264.
LibreCat
 
[291]
2015 | Conference Paper | LibreCat-ID: 10234
Hüllermeier E, Minor M. Case-Based Reasoning Research and Development . In: In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer; 2015.
LibreCat
 
[290]
2015 | Conference Paper | LibreCat-ID: 10235
Hoffmann F, Hüllermeier E. Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing. In: ; 2015.
LibreCat
 
[289]
2015 | Conference Paper | LibreCat-ID: 10236
Abdel-Aziz A, Hüllermeier E. Case Base Maintenance in Preference-Based CBR. In: In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015). ; 2015:1-14.
LibreCat
 
[288]
2015 | Conference Paper | LibreCat-ID: 10237
Szörényi B, Busa-Fekete R, Weng P, Hüllermeier E. Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In: In Proceedings International Conference on Machine Learning (ICML 2015). ; 2015:1660-1668.
LibreCat
 
[287]
2015 | Conference Paper | LibreCat-ID: 10238
Schäfer D, Hüllermeier E. Dyad Ranking Using A Bilinear Plackett-Luce Model. In: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD). ; 2015:227-242.
LibreCat
 
[286]
2015 | Conference Paper | LibreCat-ID: 10239
Hüllermeier E, Cheng W. Superset Learning Based on Generalized Loss Minimization . In: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD). ; 2015:260-275.
LibreCat
 
[285]
2015 | Conference Paper | LibreCat-ID: 10240
Henzgen S, Hüllermeier E. 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). ; 2015:422-437.
LibreCat
 
[284]
2015 | Conference Paper | LibreCat-ID: 10241
Szörényi B, Busa-Fekete R, Paul A, Hüllermeier E. Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In: In Advances in Neural Information Processing Systems 28 (NIPS 2015). ; 2015:604-612.
LibreCat
 
[283]
2015 | Conference Paper | LibreCat-ID: 10242
Szörényi B, Busa-Fekete R, Dembczynski K, Hüllermeier E. Online F-Measure Optimization. In: In Advances in Neural Information Processing Systems 28 (NIPS 2015). ; 2015:595-603.
LibreCat
 
[282]
2015 | Conference Paper | LibreCat-ID: 10243
El Mesaoudi-Paul A, Hüllermeier E. A CBR Approach to the Angry Birds Game. In: In Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015). ; 2015:68-77.
LibreCat
 
[281]
2015 | Conference Paper | LibreCat-ID: 10244
Schäfer D, Hüllermeier E. 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). ; 2015:110-111.
LibreCat
 
[280]
2015 | Conference Paper | LibreCat-ID: 10245
Lu S, Hüllermeier E. Locally weighted regression through data imprecisiation. In: Proceedings 25. Workshop Computational Intelligence. ; 2015:97-104.
LibreCat
 
[279]
2015 | Conference Paper | LibreCat-ID: 10246
Ewerth R, Balz A, Gehlhaar J, Dembczynski K, Hüllermeier E. Depth estimation in monocular images: Quantitative versus qualitative approaches. In: Proceedings 25. Workshop Computational Intelligence. ; 2015:235-240.
LibreCat
 
[278]
2015 | Journal Article | LibreCat-ID: 10319
Waegeman W, Dembczynski K, Jachnik A, Cheng W, Hüllermeier E. On the Bayes-Optimality of F-Measure Maximizers. in Journal of Machine Learning Research. 2015;15:3333-3388.
LibreCat
 
[277]
2015 | Journal Article | LibreCat-ID: 10320
Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015;281:292-299.
LibreCat
 
[276]
2015 | Journal Article | LibreCat-ID: 10321
Shaker A, Hüllermeier E. Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing. 2015;150:250-264.
LibreCat
 
[275]
2015 | Journal Article | LibreCat-ID: 10322
Hüllermeier E. From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum. 2015;38(6):500-509.
LibreCat
 
[274]
2015 | Journal Article | LibreCat-ID: 10323
Garcia-Jimenez S, Bustince U, Hüllermeier E, Mesiar R, Pal NR, Pradera A. Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems. IEEE Transactions on Fuzzy Systems. 2015;23(4):1259-1273.
LibreCat
 
[273]
2015 | Journal Article | LibreCat-ID: 10324
Senge R, Hüllermeier E. Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033.
LibreCat
 
[272]
2014 | Journal Article | LibreCat-ID: 16046
Agarwal M, Fallah Tehrani A, Hüllermeier E. Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis. 2014;22(3-4).
LibreCat
 
[271]
2014 | Journal Article | LibreCat-ID: 16060
Krotzky T, Fober T, Hüllermeier E, Klebe G. Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics. 2014;11(5):878-890.
LibreCat
 
[270]
2014 | Journal Article | LibreCat-ID: 16064
Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning. 2014;55(7):1519-1534.
LibreCat
 
[269]
2014 | Journal Article | LibreCat-ID: 16069
Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy-rule-based systems. Evolving Systems. 2014;5:175-191.
LibreCat
 
[268]
2014 | Journal Article | LibreCat-ID: 16077
Busa-Fekete R, Szörenyi B, Weng P, Cheng W, Hüllermeier E. Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning. 2014;97(3):327-351.
LibreCat
 
[267]
2014 | Journal Article | LibreCat-ID: 16078
Krempl G, Zliobaite I, Brzezinski D, et al. Open challenges for data stream mining research. SIGKDD Explorations. 2014;16(1):1-10.
LibreCat
 
[266]
2014 | Journal Article | LibreCat-ID: 16079
Strickert M, Bunte K, Schleif FM, Hüllermeier E. Correlation-based embedding of pairwise score data. Neurocomputing. 2014;141:97-109.
LibreCat
 
[265]
2014 | Journal Article | LibreCat-ID: 16080
Shaker A, Hüllermeier E. Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science. 2014;24(1):199-212.
LibreCat
 
[264]
2014 | Journal Article | LibreCat-ID: 16082
Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences. 2014;255:16-29.
LibreCat
 
[263]
2014 | Journal Article | LibreCat-ID: 16083
Donner-Banzhoff N, Haasenritter J, Hüllermeier E, Viniol A, Bösner S, Becker A. The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology. 2014;2(67):124-132.
LibreCat
 
[262]
2014 | Conference Paper | LibreCat-ID: 10247
Busa-Fekete R, Szörényi B, Hüllermeier E. PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In: Proceedings AAAI 2014, Quebec, Canada. ; 2014:1701-1707.
LibreCat
 
[261]
2014 | Conference Paper | LibreCat-ID: 10248
Busa-Fekete R, Hüllermeier E. A Survey of Preference-Based Online Learning with Bandit Algorithms. In: Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia. ; 2014:18-39.
LibreCat
 
[260]
2014 | Conference Paper | LibreCat-ID: 10249
Henzgen S, Hüllermeier E. Mining Rank Data. In: Proceedings Discovery Science, Bled,Slovenia . ; 2014:123-134.
LibreCat
 
[259]
2014 | Conference Paper | LibreCat-ID: 10250
Fallah Tehrani A, Strickert M, Hüllermeier E. The Choquet kernel for monotone data. In: Proceedings ESANN , Bruges, Belgium. ; 2014.
LibreCat
 
[258]
2014 | Conference Paper | LibreCat-ID: 10251
Abdel-Aziz A, Strickert M, Hüllermeier E. Learning Solution Similarity in Preference-Based CBR. In: Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland. ; 2014:17-31.
LibreCat
 
[257]
2014 | Conference Paper | LibreCat-ID: 10253
Schäfer D, Hüllermeier E. Dyad Ranking Using A Bilinear Plackett-Luce Model. In: Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany. ; 2014:32-33.
LibreCat
 
[256]
2014 | Conference Paper | LibreCat-ID: 10254
Calders T, Esposito F, Hüllermeier E, Meo R. Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In: Proceedings, Parts I-III. Lecture Notes in Computer Science. Springer; 2014:8724-8726.
LibreCat
 
[255]
2014 | Conference Paper | LibreCat-ID: 10295
Fürnkranz J, Hüllermeier E, Rudin C, Slowinski R, Sanner S. Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports. In: Vol 4. ; 2014:1-27.
LibreCat
 
[254]
2014 | Journal Article | LibreCat-ID: 10296
Shaker A, Hüllermeier E. Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Applied Mathematics and Computer Science. 2014;24(1):199-212.
LibreCat
 
[253]
2014 | Journal Article | LibreCat-ID: 10297
Hoffmann F, Hüllermeier E, Kroll A. Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik. 2014;62(10):685-686.
LibreCat
 
[252]
2014 | Journal Article | LibreCat-ID: 10298
Calders T, Esposito F, Hüllermeier E, Meo R. Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Data Min Knowledge Discovery. 2014;28(5-6):1129-1133.
LibreCat
 
[251]
2014 | Journal Article | LibreCat-ID: 10299
Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy rule-based systems. Evolving Systems. 2014;5(3):175-191.
LibreCat
 
[250]
2014 | Journal Article | LibreCat-ID: 10308
Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int J Approx Reasoning. 2014;55(7):1519-1534.
LibreCat
 
[249]
2014 | Journal Article | LibreCat-ID: 10309
Hüllermeier E. Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int J Approx Reasoning. 2014;55(7):1609-1613.
LibreCat
 
[248]
2014 | Journal Article | LibreCat-ID: 10310
Strickert M, Bunte K, Schleif F-M, Hüllermeier E. Correlation-based embedding of pairwise score data. Neurocomputing. 2014;141:97-109.
LibreCat
 
[247]
2014 | Journal Article | LibreCat-ID: 10311
Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences. 2014;255:16-29.
LibreCat
 
[246]
2014 | Journal Article | LibreCat-ID: 10312
Mernberger M, Moog M, Stork S, Zauner S, Maier UG, Hüllermeier E. Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J Bioinformatics and Computational Biology. 2014;12(1).
LibreCat
 
[245]
2014 | Journal Article | LibreCat-ID: 10313
Calders T, Esposito F, Hüllermeier E, Meo R. Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Machine Learning. 2014;97(1-2):1-3.
LibreCat
 
[244]
2014 | Journal Article | LibreCat-ID: 10314
Busa-Fekete R, Szörényi B, Weng P, Cheng W, Hüllermeier E. Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning. 2014;97(3):327-351.
LibreCat
 
[243]
2014 | Journal Article | LibreCat-ID: 10315
Montanés E, Senge R, Barranquero J, Quevedo JR, Del Coz JJ, Hüllermeier E. Dependent binary relevance models for multi-label classification. Pattern Recognition. 2014;47(3):1494-1508.
LibreCat
 
[242]
2014 | Journal Article | LibreCat-ID: 10316
Krempl G, Zliobaite I, Brzezinski D, et al. Open challenges for data stream mining research. SIGKDD Explorations. 2014;16(1):1-10.
LibreCat
 
[241]
2014 | Journal Article | LibreCat-ID: 10317
Krotzky T, Fober T, Hüllermeier E, Klebe G. Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Trans Comput Biology Bioinform. 2014;11(5):878-890.
LibreCat
 
[240]
2014 | Journal Article | LibreCat-ID: 10318
Stock M, Fober T, Hüllermeier E, et al. Identification of Functionally Releated Enzymes by Learning to Rank Methods. IEEE/ACM Trans Comput Biology Bioinform. 2014;11(6):1157-1169.
LibreCat
 
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