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.

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.

Hoffmann F, Hüllermeier E, Kroll A. Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik. 2014;62(10):685-686.

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.

Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy rule-based systems. Evolving Systems. 2014;5(3):175-191.

Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int J Approx Reasoning. 2014;55(7):1519-1534.

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.

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

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

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).

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.

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.

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.

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

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.

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.

Mohr F, Kleine Büning H. Semi-Automated Software Composition Through Generated Components. In: Proceedings of the 15th International Conference on Information Integration and Web-Based Applications &; Services (IiWAS). ; 2013:676-680. doi:10.1145/2539150.2539235

Mohr F, Lettmann T, Kleine Büning H. Reducing Nondeterminism in Automated Service Composition. In: Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA). ; 2013:154-161. doi:10.1109/SOCA.2013.25

Cheng W, Henzgen S, Hüllermeier E. Labelwise versus pairwise decomposition in label ranking. In: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany. ; 2013:129-136.

Senge R, del Coz J, Hüllermeier E. Rectifying classifier chains for multi-label classification, Bamberg, Germany. In: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany. ; 2013:151-158.

Busa-Fekete R, Fober T, Hüllermeier E. Preference-based evolutionary optimization using generalized racing algorithms. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2013:237-246.