Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). 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.https://doi.org/10.1145/3205455.3205562 
LibreCat: /record/2109
DOI: 10.1145/3205455.3205562

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv.
LibreCat: /record/17713

Mohr, F., Wever, M. D., &; Hüllermeier, E. (2018). Automated machine learning service composition.
LibreCat: /record/17714

Graf, H. (2018). Ranking of Classification Algorithms in AutoML. Universität Paderborn.
LibreCat: /record/5693

Scheibl, M. (2018). Learning about learning curves from dataset properties. Universität Paderborn.
LibreCat: /record/5936

Schäfer, D., &; Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-030-01771-2_11 
LibreCat: /record/6423
DOI: 10.1007/978-3-030-01771-2_11

Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C.,  Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management (Vol. 7, pp. 1–29).
LibreCat: /record/10591

Couso, I., &; Hüllermeier, E. (2018). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim, A. Nürnberger, &; C. Borgelt (Eds.), Frontiers in Computational Intelligence (pp. 31–46). Springer.
LibreCat: /record/10783

Schäfer, D., &; Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.
LibreCat: /record/16038

Ahmadi Fahandar, M., &; Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958).
LibreCat: /record/10145

El Mesaoudi-Paul, A., Hüllermeier, E., &; Busa-Fekete, R. (2018). Ranking Distributions based on Noisy Sorting. Proc. 35th Int. Conference on Machine Learning (ICML), 3469–3477.
LibreCat: /record/10148

Hesse, M., Timmermann, J., Hüllermeier, E., &; Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 15–20.
LibreCat: /record/10149

Mencia, E. L., Fürnkranz, J., Hüllermeier, E., &; Rapp, M. (2018). Learning interpretable rules for multi-label classification. In H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, &; M. A. J. van Gerven (Eds.), Explainable and Interpretable Models in Computer Vision and Machine Learning (pp. 81–113). Springer.
LibreCat: /record/10152

Nguyen, V.-L., Destercke, S., Masson, M.-H., &; Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–5095.
LibreCat: /record/10181

Schäfer, D., &; Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. Proc. 21st Int. Conference on Discovery Science (DS), 161–175.
LibreCat: /record/10184

Schäfer, D., &; Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.
LibreCat: /record/10276

Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., &; Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart &; D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft.
LibreCat: /record/1379

Ramaswamy, A., &; Bhatnagar, S. (2017). Analysis of gradient descent methods with nondiminishing bounded errors. IEEE Transactions on Automatic Control, 63(5), 1465–1471.
LibreCat: /record/24152

Ramaswamy, A., &; Bhatnagar, S. (2017). A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions. Mathematics of Operations Research, 42(3), 648–661.
LibreCat: /record/24153

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 
LibreCat: /record/3325
DOI: 10.5445/KSP/1000074341

Jakobs, M.-C., Krämer, J., van Straaten, D., &; Lettmann, T. (2017). Certiû01cation 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).
LibreCat: /record/115

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 
LibreCat: /record/1158
DOI: 10.18653/v1/W17-2206

Schnitker, N. N. (2017). Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn.
LibreCat: /record/5694

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.
LibreCat: /record/5722

Hetzer, A., &; Tornede, T. (2017). Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Universität Paderborn.
LibreCat: /record/5724

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 
LibreCat: /record/71
DOI: 10.1145/3121257.3121262

Czech, M., Hüllermeier, E., Jakobs, M.-C., &; Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions.
LibreCat: /record/72

Fürnkranz, J., &; Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005).
LibreCat: /record/10589

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.
LibreCat: /record/10784

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.
LibreCat: /record/1180

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.
LibreCat: /record/15397

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.
LibreCat: /record/15399

Couso, I., Dubois, D., &; Hüllermeier, E. (2017). Maximum likelihood estimation and coarse data. In in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain (pp. 3–16). Springer.
LibreCat: /record/15110

Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T.,  Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924).
LibreCat: /record/10204

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).
LibreCat: /record/10205

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).https://doi.org/10.1007/978-3-319-67190-1_15 
LibreCat: /record/10206
DOI: 10.1007/978-3-319-67190-1_15

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).
LibreCat: /record/10207

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).
LibreCat: /record/10208

Ahmadi Fahandar, M., &; Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence.
LibreCat: /record/10209

Hoffmann, F., Hüllermeier, E., &; Mikut, R. (2017). (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017.
LibreCat: /record/10212

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).
LibreCat: /record/10213

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.
LibreCat: /record/10216

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.
LibreCat: /record/10267

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.
LibreCat: /record/10268

Hüllermeier, E. (2017). From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection. The Computing Research Repository  (CoRR).
LibreCat: /record/10269

Ramaswamy, A., &; Bhatnagar, S. (2016). Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem. Stochastics, 88(8), 1173–1187.
LibreCat: /record/24154

Melnikov, V., Hüllermeier, E., Kaimann, D., Frick, B., &; Gupta,https://doi.org/10.4467/20838476si.16.006.6187__Pritha . (2016). Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae, 25.https://doi.org/10.4467/20838476si.16.006.6187 
LibreCat: /record/3318
DOI: 10.4467/20838476si.16.006.6187

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 
LibreCat: /record/190
DOI: 10.1109/TSE.2016.2632115

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 
LibreCat: /record/184
DOI: 10.1007/978-3-319-46227-1_47

Fürnkranz, J., &; Hüllermeier, E. (2016). Preference Learning. In C. Sammut &; G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
LibreCat: /record/10785

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 DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
LibreCat: /record/15400

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 DPL16 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany.
LibreCat: /record/15401

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 DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
LibreCat: /record/15402

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.
LibreCat: /record/15403

Schäfer, D., &; Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In in Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany.
LibreCat: /record/15404

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.
LibreCat: /record/15111

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.
LibreCat: /record/16041

Mohr, F. (2016). Towards Automated Service Composition Under Quality Constraints. Universität Paderborn.https://doi.org/10.17619/UNIPB/1-171 
LibreCat: /record/141
DOI: 10.17619/UNIPB/1-171

Fürnkranz, J., &; Hüllermeier, E. (2016). Preference Learning. In C. Sammut &; G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
LibreCat: /record/10214

Hoffmann, F., Hüllermeier, E., &; Mikut, R. (Eds.). (2016).  Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.
LibreCat: /record/10221

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.
LibreCat: /record/10222

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).
LibreCat: /record/10223

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).
LibreCat: /record/10224

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).
LibreCat: /record/10225

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.
LibreCat: /record/10226

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 DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
LibreCat: /record/10227

Schäfer, D., &; Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
LibreCat: /record/10228

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 DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
LibreCat: /record/10229

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).
LibreCat: /record/10230

Schäfer, D., &; Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In In Workshop LWDA “Lernen, Wissen, Daten, Analysen.”
LibreCat: /record/10231

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.
LibreCat: /record/10263

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.
LibreCat: /record/10264

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).
LibreCat: /record/10266

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 
LibreCat: /record/280
DOI: 10.1109/SERVICES.2015.58

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 
LibreCat: /record/323
DOI: 10.1186/s13174-015-0022-8

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 
LibreCat: /record/324
DOI: 10.1007/978-3-319-14130-5_21

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 
LibreCat: /record/319
DOI: 10.1109/SCC.2015.18

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 
LibreCat: /record/4792
DOI: 10.1109/tfuzz.2015.2396078

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).
LibreCat: /record/15406

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).
LibreCat: /record/15749

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.
LibreCat: /record/15750

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.
LibreCat: /record/15751

Senge, R., &; Hüllermeier, E. (2015). Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
LibreCat: /record/16049

Hüllermeier, E. (2015). From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.
LibreCat: /record/16051

Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
LibreCat: /record/16053

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.
LibreCat: /record/16058

Shaker, A., &; Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
LibreCat: /record/16067

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.
LibreCat: /record/10234

Hoffmann, F., &; Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.
LibreCat: /record/10235

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).
LibreCat: /record/10236

Szörényi, B., Busa-Fekete, R., Weng, P., &; Hüllermeier, E. (2015). Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In In Proceedings International Conference on Machine Learning (ICML 2015) (pp. 1660–1668).
LibreCat: /record/10237

Schäfer, D., &; Hüllermeier, E. (2015). Dyad Ranking Using A Bilinear Plackett-Luce Model. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 227–242).
LibreCat: /record/10238

Hüllermeier, E., &; Cheng, W. (2015). Superset Learning Based on Generalized Loss Minimization . In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 260–275).
LibreCat: /record/10239

Henzgen, S., &; Hüllermeier, E. (2015). Weighted Rank Correlationf: A Flexible Approach Based on Fuzzy Order Relations. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 422–437).
LibreCat: /record/10240

Szörényi, B., Busa-Fekete, R., Paul, A., &; Hüllermeier, E. (2015). Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 604–612).
LibreCat: /record/10241

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).
LibreCat: /record/10242

El Mesaoudi-Paul, A., &; Hüllermeier, E. (2015). A CBR Approach to the Angry Birds Game. In in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 68–77).
LibreCat: /record/10243

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 (MetaSel@PKDD/ECML) (pp. 110–111).
LibreCat: /record/10244

Lu, S., &; Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In Proceedings 25. Workshop Computational Intelligence (pp. 97–104).
LibreCat: /record/10245

Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., &; Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In Proceedings 25. Workshop Computational Intelligence (pp. 235–240).
LibreCat: /record/10246