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.

Henzgen S, Hüllermeier E. Weighted rank correlation measures based on fuzzy order relations. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2013:227-236.

Weng P, Busa-Fekete R, Hüllermeier E. 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. ; 2013.

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

Cheng W, Hüllermeier E. 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. ; 2013.

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

Senge R, del Coz JJ, Hüllermeier E. On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery. In: Schmidt-Thieme L, Spiliopoulou M, 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; 2013.

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

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

Nasiri N, Fober T, Senge R, Hüllermeier E. 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. ; 2013:715-721.

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

Bösner S, Bönisch K, Haasenritter  J, Schlegel P, Hüllermeier E, Donner-Banzhoff N. Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. . BMC Family Practice. 2013;14(1):154-162.

Haasenritter J, Viniol A, Becker A, et al. Diagnose im Kontext - eine erweiterte Perspektive. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen (ZEFQ). 2013;107:585-591.

Shaker A, Senge R, Hüllermeier E. Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences. 2013;220:34-45.

Szarvas G, Busa-Fekete R, Hüllermeier E. Learning to rank lexical substitutions. In: In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA. ; 2013.

Dembczynski K, Jachnik A, Kotlowski W, Waegeman W, Hüllermeier E. Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization. In: Dasgupta S, McAllester D, eds. In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA. ; 2013:1130-1138.

Busa-Fekete R, Szoreny B, Weng P, Cheng W, Hüllermeier E. Top-k selection based on adaptive sampling of noisy preferences. In: Dasgupta S, McAllester D, eds. In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA. ; 2013:1094-1102.

Hüllermeier E, Cheng W. Preference-based CBR: General ideas and basic principles. In: Rossi F, ed. In Proceedings IJCAI-13, 23rd International Joint Conference on Artificial Intelligence, Beijing, China. AAAI Press; 2013:3012-3016.