Busa-Fekete, R., Szörényi, B., Weng, P., Cheng, W., &; Hüllermeier, E. (2014). Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.

Montanés, E., Senge, R., Barranquero, J., Quevedo, J. R., Del Coz, J. J., &; Hüllermeier, E. (2014). Dependent binary relevance models for multi-label classification. Pattern Recognition, 47(3), 1494–1508.

Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V.,  Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.

Krotzky, T., Fober, T., Hüllermeier, E., &; Klebe, G. (2014). Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Trans. Comput. Biology Bioinform., 11(5), 878–890.

Stock, M., Fober, T., Hüllermeier, E., Glinca, S., Klebe, G., Pahikkala, T.,  Wageman, W. (2014). Identification of Functionally Releated Enzymes by Learning to Rank Methods. IEEE/ACM Trans. Comput. Biology Bioinform., 11(6), 1157–1169.

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

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

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

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

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

Henzgen, S., &; Hüllermeier, E. (2013). Weighted rank correlation measures based on fuzzy order relations. In F. Hoffmann &; E. Hüllermeier (Eds.), in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany (pp. 227–236). KIT Scientific Publishing.

Weng, P., Busa-Fekete, R., &; Hüllermeier, E. (2013). Interactive Q-learning with ordinal rewards and unreliable tutor. In In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague.

Busa-Fekete, R., Szörenyi, B., Weng, P., &; Hüllermeier, E. (2013). Preference-based evolutionary direct policy search. In In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague.

Cheng, W., &; Hüllermeier, E. (2013). A nearest neigbor approach to label ranking based on generalized labelwise loss minimization. In In Proceedings M-PREF, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China.

Shaker, A., &; Hüllermeier, E. (2013). Event history analysis on data streams: An application to earthquake occurence. In G. Krempl, I. Zliobaite, Y. Wang, &; G. Forman (Eds.), In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic (pp. 38–41).

Senge, R., del Coz, J. J., &; Hüllermeier, E. (2013). On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery. In L. Schmidt-Thieme &; M. Spiliopoulou (Eds.), In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany . Springer.

Fober, T., Klebe, G., &; Hüllermeier, E. (2013). Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics, Algorithms from and for Nature and Life. In B. Lausen, D. Van den Poel, &; A. Ultsch (Eds.), In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany (pp. 279–286). Springer.

Fallah Tehrani, A., &; Hüllermeier, E. (2013). Ordinal Choquistic regression . In J. Montero, G. Pasi, &; D. Ciucci (Eds.), in Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy. Atlantis Press.

Nasiri, N., Fober, T., Senge, R., &; Hüllermeier, E. (2013). Fuzzy Pattern Trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of colour yield in poyester dyeing, Edmonton, Canada. In in Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada (pp. 715–721).

Heider, D., Senge, R., Cheng, W., &; Hüllermeier, E. (2013). Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction. Bioinformatics, 29(16), 1946–1952.