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449 Publications
2017 | Conference Paper | LibreCat-ID: 10213
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 (pp. 1–12).
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2017 | Conference Paper | LibreCat-ID: 10216
Shaker, A., Heldt, W., & Hüllermeier, E. (2017). Learning TSK Fuzzy Rules from Data Streams. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia.
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2017 | Journal Article | LibreCat-ID: 10267
Bräuning, M., Hüllermeier, E., Keller, T., & Glaum, M. (2017). Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application in accounting. European Journal of Operational Research, 258(1), 295–306.
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2017 | Journal Article | LibreCat-ID: 10268
Platenius, M.-C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2017). Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Transactions on Software Engineering, 43(8), 739–759.
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2017 | Journal Article | LibreCat-ID: 10269
Hüllermeier, E. (2017). From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection. The Computing Research Repository (CoRR).
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2016 | Journal Article | LibreCat-ID: 24154
Ramaswamy, A., & Bhatnagar, S. (2016). Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem. Stochastics, 88(8), 1173–1187.
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2016 | Journal Article | LibreCat-ID: 3318
Melnikov, V., Hüllermeier, E., Kaimann, D., Frick, B., & Gupta, Pritha . (2016). Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae, 25. https://doi.org/10.4467/20838476si.16.006.6187
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2016 | Journal Article | LibreCat-ID: 190
Platenius, M. C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2016). Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, (8), 739–759. https://doi.org/10.1109/TSE.2016.2632115
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2016 | Conference Paper | LibreCat-ID: 184
Melnikov, V., & Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) (pp. 756–771). https://doi.org/10.1007/978-3-319-46227-1_47
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2016 | Encyclopedia Article | LibreCat-ID: 10785
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference Paper | LibreCat-ID: 15400
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the identifiability of models in multi-criteria preference learning. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Conference Paper | LibreCat-ID: 15401
Schäfer, D., & Hüllermeier, E. (2016). Preference -based reinforcement learning using dyad ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany.
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2016 | Conference Paper | LibreCat-ID: 15402
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Conference Paper | LibreCat-ID: 15403
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy superset losses. In E. Hüllermeier, F. Hoffmann, & R. Mikut (Eds.), in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany (pp. 1–8). KIT Scientific Publishing.
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2016 | Conference Paper | LibreCat-ID: 15404
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In in Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany.
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2016 | Conference Paper | LibreCat-ID: 15111
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
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2016 | Journal Article | LibreCat-ID: 16041
Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., & Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
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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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| DOI
2016 | Book Chapter | LibreCat-ID: 10214
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference (Editor) | LibreCat-ID: 10221
Hoffmann, F., Hüllermeier, E., & Mikut, R. (Eds.). (2016). Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.
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2016 | Conference Paper | LibreCat-ID: 10222
Jasinska, K., Dembczynski, K., Busa-Fekete, R., Klerx, T., & Hüllermeier, E. (2016). Extreme F-measure maximization using sparse probability estimates . In M. F. Balcan & K. Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA.
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2016 | Conference Paper | LibreCat-ID: 10223
Melnikov, V., & Hüllermeier, E. (2016). Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016. In European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 756–771).
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2016 | Conference Paper | LibreCat-ID: 10224
Dembczynski, K., Kotlowski, W., Waegeman, W., Busa-Fekete, R., & Hüllermeier, E. (2016). Consistency of probalistic classifier trees. In In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 511–526).
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2016 | Conference Paper | LibreCat-ID: 10225
Shabani, A., Paul, A., Platon, R., & Hüllermeier, E. (2016). Predicting the electricity consumption of buildings: An improved CBR approach. In In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA (pp. 356–369).
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2016 | Conference Paper | LibreCat-ID: 10226
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
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2016 | Conference Paper | LibreCat-ID: 10227
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the Identifiability of models in multi-criteria preference learning . In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10228
Schäfer, D., & Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10229
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10230
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy supersets losses. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing (pp. 1–8).
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2016 | Conference Paper | LibreCat-ID: 10231
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In In Workshop LWDA “Lernen, Wissen, Daten, Analysen.”
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2016 | Conference (Editor) | LibreCat-ID: 10263
Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F., & van Harmelen, F. (Eds.). (2016). ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence (Vol. 285). The Hague, The Netherlands: IOS Press.
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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 | Journal Article | LibreCat-ID: 10266
Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., & Heider, D. (2016). Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining, 9(10).
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2015 | Conference Paper | LibreCat-ID: 280
Arifulina, S., Platenius, M. C., Mohr, F., Engels, G., & Schäfer, W. (2015). Market-Specific Service Compositions: Specification and Matching. In Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet (pp. 333--340). https://doi.org/10.1109/SERVICES.2015.58
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2015 | Journal Article | LibreCat-ID: 323
Jungmann, A., & Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications, (1), 1–18. https://doi.org/10.1186/s13174-015-0022-8
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2015 | Conference Paper | LibreCat-ID: 324
Mohr, F. (2015). A Metric for Functional Reusability of Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 298--313). https://doi.org/10.1007/978-3-319-14130-5_21
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2015 | Conference Paper | LibreCat-ID: 319
Mohr, F., Jungmann, A., & Kleine Büning, H. (2015). Automated Online Service Composition. In Proceedings of the 12th IEEE International Conference on Services Computing (SCC) (pp. 57--64). https://doi.org/10.1109/SCC.2015.18
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2015 | Journal Article | LibreCat-ID: 4792
Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033. https://doi.org/10.1109/tfuzz.2015.2396078
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2015 | Conference Paper | LibreCat-ID: 15406
Schäfer, D., & Hüllermeier, E. (2015). Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal (pp. 110–111).
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2015 | Conference Paper | LibreCat-ID: 15749
Paul, A., & Hüllermeier, E. (2015). A cbr approach to the angry birds game. In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany (pp. 68–77).
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2015 | Conference Paper | LibreCat-ID: 15750
Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., & Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In F. Hoffmann & E. Hüllermeier (Eds.), In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany (pp. 235–240). KIT Scientific Publishing.
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2015 | Conference Paper | LibreCat-ID: 15751
Lu, S., & Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany (pp. 97–104). KIT Scientific Publishing.
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2015 | Journal Article | LibreCat-ID: 16049
Senge, R., & Hüllermeier, E. (2015). Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
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2015 | Journal Article | LibreCat-ID: 16051
Hüllermeier, E. (2015). From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.
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2015 | Journal Article | LibreCat-ID: 16053
Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
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2015 | Journal Article | LibreCat-ID: 16058
Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., & Hüllermeier, E. (2015). On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research, 15, 3313–3368.
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2015 | Journal Article | LibreCat-ID: 16067
Shaker, A., & Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
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2015 | Conference Paper | LibreCat-ID: 10234
Hüllermeier, E., & Minor, M. (2015). Case-Based Reasoning Research and Development . In in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer.
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2015 | Conference Paper | LibreCat-ID: 10235
Hoffmann, F., & Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.
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2015 | Conference Paper | LibreCat-ID: 10236
Abdel-Aziz, A., & Hüllermeier, E. (2015). Case Base Maintenance in Preference-Based CBR. In In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 1–14).
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