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402 Publications


2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar, M., Hüllermeier, E., & Couso, I. (2017). Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).
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2017 | Conference Paper | LibreCat-ID: 10212
Hoffmann, F., Hüllermeier, E., & Mikut, R. (2017). (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017.
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2017 | Journal Article | LibreCat-ID: 10267
Bräuning, M., Hüllermeier, E., Keller, T., & Glaum, M. (2017). Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting. European Journal of Operational Research, 258(1), 295–306.
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2017 | Conference Paper | LibreCat-ID: 15399
Czech, M., Hüllermeier, E., Jacobs, M. C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany.
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2017 | Conference Abstract | LibreCat-ID: 5722
Gupta, P., Hetzer, A., Tornede, T., Gottschalk, S., Kornelsen, A., Osterbrink, S., … Hüllermeier, E. (2017). jPL: A Java-based Software Framework for Preference Learning. Presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock.
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2017 | Conference Paper | LibreCat-ID: 10206
Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206).
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2017 | Conference Paper | LibreCat-ID: 10213
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 (pp. 1–12).
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2017 | Journal Article | LibreCat-ID: 10268
Platenius, M.-C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2017). Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Transactions on Software Engineering, 43(8), 739–759.
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2017 | Conference Paper | LibreCat-ID: 10207
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26).
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2017 | Journal Article | LibreCat-ID: 10269
Hüllermeier, E. (2017). From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection. The Computing Research Repository  (CoRR).
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2017 | Conference Paper | LibreCat-ID: 115
Jakobs, M.-C., Krämer, J., van Straaten, D., & Lettmann, T. (2017). Certification Matters for Service Markets. In T. P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) (pp. 7–12).
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2017 | Mastersthesis | LibreCat-ID: 5724
Hetzer, A., & Tornede, T. (2017). Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Paderborn.
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2017 | Conference Paper | LibreCat-ID: 71
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262
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2017 | Book Chapter | LibreCat-ID: 10784
Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (Vol. 107, pp. 1000–1005). Springer.
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2017 | Conference Paper | LibreCat-ID: 10208
Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum Likelihood Estimation and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) (pp. 3–16).
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2017 | Conference Paper | LibreCat-ID: 1158
Seemann, N., Merten, M.-L., Geierhos, M., Tophinke, D., & Hüllermeier, E. (2017). Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 40–45). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W17-2206
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2017 | Conference Paper | LibreCat-ID: 15397
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the structure of nested dichotomies. A comparison of two heuristics. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany (pp. 1–12). KIT Scientific Publishing.
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2017 | Conference Paper | LibreCat-ID: 3325
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000074341
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2017 | Report | LibreCat-ID: 72
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions.
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2016 | Encyclopedia Article | LibreCat-ID: 10785
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference Paper | LibreCat-ID: 10223
Melnikov, V., & Hüllermeier, E. (2016). Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016. In European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 756–771).
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2016 | Conference Paper | LibreCat-ID: 10228
Schäfer, D., & Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10230
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy supersets losses. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing (pp. 1–8).
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2016 | Journal Article | LibreCat-ID: 10266
Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., & Heider, D. (2016). Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining, 9(10).
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2016 | Conference Paper | LibreCat-ID: 15401
Schäfer, D., & Hüllermeier, E. (2016). Preference -based reinforcement learning using dyad ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany.
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2016 | Conference Paper | LibreCat-ID: 10224
Dembczynski, K., Kotlowski, W., Waegeman, W., Busa-Fekete, R., & Hüllermeier, E. (2016). Consistency of probalistic classifier trees. In In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 511–526).
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2016 | Conference Paper | LibreCat-ID: 10229
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10231
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In In Workshop LWDA “Lernen, Wissen, Daten, Analysen.”
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2016 | Conference Paper | LibreCat-ID: 15402
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Conference Paper | LibreCat-ID: 15111
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
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2016 | Conference Paper | LibreCat-ID: 10225
Shabani, A., Paul, A., Platon, R., & Hüllermeier, E. (2016). Predicting the electricity consumption of buildings: An improved CBR approach. In In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA (pp. 356–369).
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2016 | Conference (Editor) | LibreCat-ID: 10263
Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F., & van Harmelen, F. (Eds.). (2016). ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence (Vol. 285). The Hague, The Netherlands: IOS Press.
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2016 | Conference Paper | LibreCat-ID: 15403
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy superset losses. In E. Hüllermeier, F. Hoffmann, & R. Mikut (Eds.), in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany (pp. 1–8). KIT Scientific Publishing.
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2016 | Journal Article | LibreCat-ID: 190
Platenius, M. C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2016). Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, (8), 739–759. https://doi.org/10.1109/TSE.2016.2632115
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2016 | Book Chapter | LibreCat-ID: 10214
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference (Editor) | LibreCat-ID: 10221
Hoffmann, F., Hüllermeier, E., & Mikut, R. (Eds.). (2016). Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.
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2016 | Conference Paper | LibreCat-ID: 10226
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
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2016 | Journal Article | LibreCat-ID: 10264
Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., & Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
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2016 | Dissertation | LibreCat-ID: 141
Mohr, F. (2016). Towards Automated Service Composition Under Quality Constraints. Universität Paderborn. https://doi.org/10.17619/UNIPB/1-171
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2016 | Conference Paper | LibreCat-ID: 15404
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In in Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany.
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2016 | Journal Article | LibreCat-ID: 16041
Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., & Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
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2016 | Conference Paper | LibreCat-ID: 184
Melnikov, V., & Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) (pp. 756–771). https://doi.org/10.1007/978-3-319-46227-1_47
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2016 | Conference Paper | LibreCat-ID: 10222
Jasinska, K., Dembczynski, K., Busa-Fekete, R., Klerx, T., & Hüllermeier, E. (2016). Extreme F-measure maximization using sparse probability estimates . In M. F. Balcan & K. Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA.
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2016 | Conference Paper | LibreCat-ID: 10227
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the Identifiability of models in multi-criteria preference learning . In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 15400
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the identifiability of models  in multi-criteria preference learning. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Journal Article | LibreCat-ID: 3318
Melnikov, V., Hüllermeier, E., Kaimann, D., Frick, B., & Gupta, Pritha . (2016). Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae, 25. https://doi.org/10.4467/20838476si.16.006.6187
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2015 | Journal Article | LibreCat-ID: 10324
Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
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2015 | Conference Paper | LibreCat-ID: 10235
Hoffmann, F., & Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.
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2015 | Conference Paper | LibreCat-ID: 10242
Szörényi, B., Busa-Fekete, R., Dembczynski, K., & Hüllermeier, E. (2015). Online F-Measure Optimization. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 595–603).
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2015 | Conference Paper | LibreCat-ID: 15406
Schäfer, D., & Hüllermeier, E. (2015). Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal (pp. 110–111).
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