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.

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.

Senge R, Hüllermeier E. Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033.

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

Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015;281:292-299.

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.

Shaker A, Hüllermeier E. Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing. 2015;150:250-264.

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.

Hoffmann F, Hüllermeier E. Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing. In: ; 2015.

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.

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.

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.

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.

Henzgen S, Hüllermeier E. 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). ; 2015:422-437.

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.

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.

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.

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.

Lu S, Hüllermeier E. Locally weighted regression through data imprecisiation. In: Proceedings 25. Workshop Computational Intelligence. ; 2015:97-104.

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.

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.

Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015;281:292-299.

Shaker A, Hüllermeier E. Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing. 2015;150:250-264.

Hüllermeier E. From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum. 2015;38(6):500-509.

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.

Senge R, Hüllermeier E. Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033.

Basavaraju M, Chandran LS, Rajendraprasad D, Ramaswamy A. Rainbow connection number of graph power and graph products. Graphs and Combinatorics. 2014;30(6):1363-1382.

Basavaraju M, Chandran LS, Rajendraprasad D, Ramaswamy A. Rainbow connection number and radius. Graphs and Combinatorics. 2014;30(2):275-285.

Mohr F, Walther S. Template-based Generation of Semantic Services. In: Proceedings of the 14th International Conference on Software Reuse (ICSR). LNCS. ; 2014:188-203. doi:10.1007/978-3-319-14130-5_14

Jungmann A, Mohr F, Kleinjohann B. Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. In: Proceedings of the 10th World Congress on Services (SERVICES). ; 2014:346-353. doi:10.1109/SERVICES.2014.68

Jungmann A, Mohr F, Kleinjohann B. Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. In: Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA). ; 2014:105-112. doi:10.1109/SOCA.2014.48

Mohr F. Estimating Functional Reusability of Services. In: Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC). LNCS. ; 2014:411-418.

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).

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.

Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning. 2014;55(7):1519-1534.

Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy-rule-based systems. Evolving Systems. 2014;5:175-191.

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.

Krempl G, Zliobaite I, Brzezinski D, et al. Open challenges for data stream mining research. SIGKDD Explorations. 2014;16(1):1-10.

Strickert M, Bunte K, Schleif FM, Hüllermeier E. Correlation-based embedding of pairwise score data. Neurocomputing. 2014;141:97-109.

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.

Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences. 2014;255:16-29.

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.

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.

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.

Henzgen S, Hüllermeier E. Mining Rank Data. In: Proceedings Discovery Science, Bled,Slovenia . ; 2014:123-134.

Fallah Tehrani A, Strickert M, Hüllermeier E. The Choquet kernel for monotone data. In: Proceedings ESANN , Bruges, Belgium. ; 2014.

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.

Schäfer D, Hüllermeier E. Dyad Ranking Using A Bilinear Plackett-Luce Model. In: Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany. ; 2014:32-33.

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.

Fürnkranz J, Hüllermeier E, Rudin C, Slowinski R, Sanner S. Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports. In: Vol 4. ; 2014:1-27.