Dembczynski K, Waegeman W, Cheng W, Hüllermeier E. An exact algorithm for F-measure maximization. In: In Proceedings NIPS-2011, 25th Annual Conference on Neural Information Processing Systems, Granada, Spain. ; 2011.

Fürnkranz J, Hüllermeier E. Learning from label preferences. In: Elomaa T, Hollmen J, Mannila H, eds. In Proceedings DS-2011, 14th International  Conference on Discovery Science, Number 6926 in LNAI. Springer; 2011:2-17.

Fallah Tehrani A, Cheng W, Dembczynski K, Hüllermeier E. Learning  monotone nonlinear models using the Choquet integral. In: In Proceedings ECML/PKDD-2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece,. ; 2011.

Hüllermeier E, Schlegel P. Preference-based CBR: First steps toward a methodological framework. In: Ram A, Wiratunga N, eds. In Proceedings ICCBR-2011, 19th International Conference on Case-Based Reasoning, Number 6880 in LNAI. Springer; 2011:77-91.

Kotlowski W, Dembczynski K, Hüllermeier E. Bipartite ranking through minimization of univariate loss. In: In Proceedings ICML-2011, 28th International Conference on Machine Learning, Washington, USA. ; 2011.

Hüllermeier E, Hühn J. An Analysis of the FURIA algorithm for fuzzy rule induction. In: Koronacki J, Ras ZW, Wierzchon ST, Kacprzyk J, eds. Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S.Michalski. Vol 262. Studies in Computational Intelligence. Springer; 2010:321-344.

Fürnkranz J, Hüllermeier E. Preference Learning: An Introduction. In: Fürnkranz J, Hüllermeier E, eds. Preference Learning. Springer; 2010:1-18.

Fober T, Mernberger M, Klebe G, Hüllermeier E. Efficient similarity retrieval for protein binding sites based on histogram comparison. In: In Proceedings GCB-2010 German Conference on Bioinformatics Braunschweig, Germany. ; 2010:51-60.

Dembczynski K, Waegeman W, Cheng W, Hüllermeier E. On label dependence in multi-label classification. In: In Proceedings MLD-2010, 2nd International Workshop on Learning from Multi-Label Data, Haifa, Israel. ; 2010.

Fober T, Hüllermeier E. Similarity measures for protein structures based on fuzzy histogram comparison. In: In Proceedings WCCI-2010, World Congress on Computational Intelligence, Barcelona, Spain. ; 2010.

Senge R, Hüllermeier E. Pattern trees for regression and fuzzy systems modeling. In: In Proceedings WCCI-2010, World Congress on Computational Intelligence, Barcelona, Spain. ; 2010.

Pfeffer P, Fober T, Hüllermeier E, Klebe G. GARLig: A fully automated tool for subset selection of large fragment spaces via a self-adaptive genetic algorithm. Journal of Chemical Information and Modeling. 2010;50(9):1644-1659.

Hüllermeier E, Vanderlooy S. Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognition. 2010;43(1):128-142.

Hüllermeier E, Fürnkranz J. On predictive accuracy and risk minimization in pairwise label ranking. Journal of Computer and System Sciences. 2010;76(1):49-62.

Dembczynski K, Cheng W, Hüllermeier E. Bayes optimal multilabel classification via probalistic classifier chains. In: Fürnkranz J, Joachims T, eds. In Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel. ; 2010:279-286.

Cheng W, Dembczynski K, Hüllermeier E. Label ranking based on the Plackett-Luce model. In: Fürnkranz J, Joachims T, eds. In Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel. ; 2010:215-222.

Cheng W, Dembczynski K, Hüllermeier E. Graded multi-label classification: The ordinal case. In: Fürnkranz J, Joachims T, eds. In Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel. ; 2010.

Dembczynski K, Waegeman W, Cheng W, Hüllermeier E. Regret analysis for performance metrics in multi-label classification: The case of Hamming and subset zero-one loss. In: In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain. ; 2010.

Cheng W, Rademaker M, De Beats B, Hüllermeier E. Predicting partial orders: Ranking with abstention. In: In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain. ; 2010.

Hüllermeier E. Uncertainty in clustering and classification . In: In Proceedings SUM 2010, International Conference on Scalable Uncertainty Management . Vol 6379. Springer; 2010:16-19.

Cheng W, Hüllermeier E. A simple instance-based approach to multilabel classification using the Mallows model. In: In Proceedings MLD-2009 1st  International Workshop on Learning from Multi-Label Data, Bled, Slovenia. ; 2009:28-38.

Senge R, Hüllermeier E. Learning pattern tree classifiers using a co-evolutionary algorithm. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 19th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2009:22-33.

Fober T, Mernberger M, Moritz R, Hüllermeier E. Graph-kernels for the comparative analysis of protein active sites. In: Grosse I, Neumann S, Posch S, Schreiber F, Stadler P., eds. In Proceedings GCB-2009 German Conference on Bioinformatics Halle (Saale), Germany. ; 2009:21-31.

Senge R, Hüllermeier E. Learning pattern tree classifiers using a co-evolutionary algorithm. In: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Darmstadt, Germany. ; 2009:105-110.

Fober T, Mernberger M, Melnikov V, Moritz R, Hüllermeier E. Extension and empirical comparison of graph-kernels for the analysis of protein active sites. In: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Darmstadt, Germany. ; 2009:30-36.

Fober T, Klebe G, Hüllermeier E. Efficient construction of multiple geometrical alignments for the comparison of protein binding sites. In: In Proceedings ISDA-2009, 9th International Conference on Intelligent Systems Design and Applications, Pisa, Italy. ; 2009:1251-1256.

Boukhris I, Elouedi Z, Fober T, Mernberger M, Hüllermeier E. Similarity analysis of protein binding sites: A generalization of the maximum common subgraph measure based on quasi-clique detection. In: In Proceedings IDA-2009, 9th International Symposium on Intelligent Data Analysis, Pisa / Italy. ; 2009:1245-1250.

Fober T, Hüllermeier E. Fuzzy modeling of labeled point cloud superposition  for the comparison of protein binding sites. In: In Proceedings IFSA / EUSFLAT-2009 World Congress of  the Fuzzy Systems Association, Lissabon, Portugal. ; 2009:1299-1304.

Hüllermeier E, Rifqi M. A fuzzy variant of the Rand index for comparing clustering structures. In: In Proceedings IFSA / EUSFLAT-2009 World Congress of  the Fuzzy Systems Association, Lissabon, Portugal. ; 2009:1294-1298.

Weskamp N, Hüllermeier E, Klebe G. Merging chemical and biological space: Structural mapping of enzyme binding pocket space. Proteins. 2009;76(2):317-330.

Hühn J, Hüllermeier E. FURIA: An Algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery . 2009;19:293-319.

Hüllermeier E, Vanderlooy S. Why fuzzy decision trees are good rankers. IEEE Transactions on Fuzzy Systems. 2009;17(6):1233-1244.

Yi Y, Fober T, Hüllermeier E. Fuzzy operator trees for modeling rating functions. International Journal of Computational Intelligence and Applications. 2009;8(4):413-428.

Fober T, Mernberger M, Klebe G, Hüllermeier E. Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. Bioinformatics. 2009;25(16):2110-2117.

Hühn J, Hüllermeier E. F: A fuzzy rule learner for inducing reliable classifiers. IEEE Transactions on Fuzzy Systems. 2009;17(1):138-149.

Hüllermeier E, Vladimirskiy I, Prados Suarez B, Stauch E. Supporting case-based retrieval by similarity skylines: Basic concepts and extensions. Künstliche Intelligenz. 2009;1(09):24-29.

Hüllermeier E. On the usefulness of fuzzy sets in data mining. In: Seising R, ed. Views on Fuzzy Sets and Systems from Different Perspectives: Philosophy and Logic, Criticisms and Applications. Springer; 2009:457-470.

Cheng W, Hühn J, Hüllermeier E. Decision tree and instance-based learning for label ranking. In: In Proceedings ICML-2009, 26th International Conference on Machine Learning, Montreal, Canada. ; 2009:161-168.

Fürnkranz J, Hüllermeier E, Vanderlooy S. Binary decomposition methods for multipartite ranking. In: In Proceedings ECML/PKDD-2009, European Conference on Machine Learning and Principles and Knowledge Discovery in Databases, Bled Sloveniaery in Databases, Bled, Slovenia. ; 2009.

Cheng W, Hüllermeier E. A new instance-based label ranking approach using the Mallows model. In Advances in Neural Networks. In: In Proceedings 6th International Symposium on Neural Networks, Wuhan, China. Springer; 2009:707-716.

Hüllermeier E, Vanderlooy S. Weighted voting as approximate MAP prediction in pairwise classification. In: In Proceedings Workshop LWA-2008, Lernen-Wissensentdeckung-Adaptivität, Würzburg, Germany. ; 2008:34-41.

Fober T, Hüllermeier E, Mernberger M. Evolutionary construction of multiple graph alignments for mining structured biomolecular data . In: In Proceedings Workshop LWA-2008, Lernen-Wissensentdeckung-Adaptivität, Würzburg, Germany. ; 2008:27-33.

Fober T, Hüllermeier E, Mernberger M. Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. In: Beyer A, Schroeder M, eds. IIn Proceedings GCB-2008, Germany Conference on Bioinformatics, Dresden 2008. ; 2008:44-53.

Hühn J, Hüllermeier E. Is an ordinal class structure useful in classifier learning? International Journal of Data Mining, Modeling and Management . 2008;1(1):45-67.

Hüllermeier E, Fürnkranz J, Cheng W, Brinker K. Label ranking by learning pairwise preferences. Artificial Intelligence. 2008;172:1897-1917.

Vanderlooy S, Hüllermeier E. A critical analysis of variants of the AUC. Machine Learning. 2008;72:247-272.

Fürnkranz J, Hüllermeier E, Mencia E, Brinker K. Multilabel classification via calibrated label ranking. Machine Learning. 2008;73(2):133-153.

Hüllermeier E, Brinker K. Learning valued preference structures for solving classification problems. Fuzzy Sets and Systems. 2008;159(18):2337-2352.

Beringer J, Hüllermeier E. Case-based learning in a bipolar possibilistic framework. International Journal of Intelligent Systems. 2008;23(10):1119-1134.

Hüllermeier E. Granular computing in machine learning and data mining. In: Pedrycz W, Skowron A, Kreinovich V, eds. Handbook on Granular Computing. John Wiley and Sons; 2008:889-906.