Nasiri M, Hüllermeier E, Senge R, Lughofer E. Comparing methods for knowledge-driven and data-driven fuzzy modeling: A case study in textile industry. In: In Proceedings IFSA-2011 World Congress of the International Fuzzy Systems Association, Surabaya and Bali Island, Indonesia. ; 2011:RW-103-1-6.

Lughofer E, Hüllermeier E. On-line redundancy deletion in evolving fuzzy regression models using a fuzzy inclusion measure. In: Galichet S, Montero J, Mauris G, eds. In Proceedings EUSFLAT-2011 7th International Conference on the European Society for Fuzzy Logic and Technology, Aix-Les-Bains, France. ; 2011:380-387.

Fallah Tehrani A, Cheng W, Hüllermeier E. Choquistic regression: Generalizing logistic regression using the Choquet integral. In: In Proceedings EUSFLAT-2011 7th International Conference on the European Society for Fuzzy Logic and Technology, Aix-Les-Bains, France. ; 2011:868-875.

Hirsch O, Bösner S, Hüllermeier E, Senge R, Dembczynski K, Donner-Banzhoff N. Multivariate modeling to identify patterns in clinical data: The example of chest pain. BMC Medical Research Methodology. 2011;11(155).

Domshlak C, Hüllermeier E, Kaci S, Prade H. Preferences in AL: An overview. Artificial Intelligence. 2011;175(7-8):1037-1052.

Hüllermeier E. Fuzzy machine learning and data mining. WIREs Data Mining and Knowledge Discovery. 2011;1(4):269-283.

Mernberger M, Klebe G, Hüllermeier E. SEGA: Semi-global graph alignment for structure-based protein comparison. IEEE/ACM Transactions of Computational Biology and Bioinformatics. 2011;8(5):1330-1343.

Fober T, Glinca S, Klebe G, Hüllermeier E. Superposition and alignment of labeled point clouds. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2011;8(6):1653-1666.

Hüllermeier E. Fuzzy sets in machine learning and data mining. Applied Soft Computing Journal. 2011:1493-1505.

Senge R, Hüllermeier E. Top-down induction of fuzzy pattern trees. IEEE Transactions on Fuzzy Systems. 2011;19(2):241-252.

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.

Hüllermeier E. Fuzzy methods in data mining. In: Encyclopedia of Data Warehousing and Mining - Second Edition. Idea Group, Inc.,Hershey, USA; 2008:907-912.

Hüllermeier E, Fürnkranz J. Learning preference models from data: On the problem of label ranking and its variants. In: Della Riccia G, Dubois D, Kruse R, Lenz HJ, eds. Preferences and Similarities. Preferences and Similarities. Springer; 2008:283-304.

Hüllermeier E, Vladimirskiy I, Prados Suarez B, Stauch E. Supporting case-based retrieval by similarity skylines: Basic concepts and extensions. In: Althoff KD, Bergmann R, Minor M, Hanft A, eds. In Proceedings ECCBR-2008 9th European Conference on Case-Based  Reasoning, Trier, Germany. ; 2008:240-254.

Cheng W, Hüllermeier E. Learning similarity functions from qualitative feedback. In: Althoff KD, Bergmann R, Minor M, Hanft A, eds. In Proceedings ECCBR-2008 9th European Conference on Case-Based  Reasoning, Trier, Germany. Springer; 2008:120-134.

Cheng W, Hüllermeier E, Seeger B, Vladimirskiy I. Interactive ranking of skylines using machine learning techniques. In: In Proceedings Workshop LWA-2007, Lernen-Wissensentdeckung-Adaptivität, Halle/Saale, Germany. ; 2007:141-148.

Fober T, Hüllermeier E, Mernberger M. Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. In: Mikut R, Reischl M, eds. In Proceedings 17th Workshop Computational Intelligence, Dortmund Germany. ; 2007:1-14.

Vladimirskiy I, Hüllermeier E, Stauch E. Similarity search over uncertain archaeological data using a modified skyline operator. In: Wilson DC, Khemani D, eds. In Workshop Proceedings from ICCBR-2007 7th International Conference on Case-Based Reasoning, Belfast, Northern Ireland. ; 2007:31-40.

Hüllermeier E, Fürnkranz J. On minimizing the position error in label ranking. In: In Proceedings ECML-2007, 17th European Conference on Machine Learning, Warsawa, Poland, September 2007. Springer; 2007.

Brinker K, Hüllermeier E. Label ranking in case-based reasoning. In: Richter M, Weber R, eds. In Proceedings ICCBR-2007, 7th International Conference on Case-Based Reasoning, Belfast, Northern Ireland. Vol 4626. LNAI. Springer; 2007:77-91.

Beringer J, Hüllermeier E. Efficient instance-based learning on data streams. In: Perner P, ed. In Proceedings ICDM-2007, International Conference on Industrial Data Mining, Leipzig. Springer; 2007:34-48.

Brinker K, Hüllermeier E. Case-based multilabel ranking. In: In Proceedings IJCAI-07, 20th International Joint Conference on Artificial Intelligence, Hyerabad, India, January 2007. ; 2007:701-707.

Hüllermeier E, Brinker K. Fuzzy-relational classificatonf: Combining pairwise decomposition techniques with fuzzy preference modeling. In: In Proceedings EUSFLAT-2007 5th International Conference on the European Society for Fuzzy Logic and Technology, Ostrava, Czech Republic. Vol 1. ; 2007:353-360.

Beringer J, Hüllermeier E. Adaptive optimization of the number of clusters in fuzzy clustering, London 2007. In: In Proceedings FUZZ-IEEE-07, IEEE International Conference on Fuzzy Systems. ; 2007.

Hüllermeier E, Weskamp N, Klebe G, Kuhn D. Graph alignment: Fuzzy pattern mining for the structural analysis of protein active sites. In: In Proceedings FUZZ-IEEE-07, IEEE International Conference on Fuzzy Systems, London. ; 2007.

Hüllermeier E, Yi Y. In defense of fuzzy association analysis. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics. 2007;37(4):1039-1043.

Hüllermeier E. Credible case-based inference using similarity profiles. IEEE Transactions on Knowledge and Data Engineering. 2007;19(5):847-858.

Beringer J, Hüllermeier E. Efficient instance-based learning on data streams. Intelligent Data Analysis. 2007;11(6):627-650.

Dubois D, Hüllermeier E. Comparing probability measures using possibility theory: A notion of relative peakedness. International Journal of Approximate Reasoning. 2007;45(2):364-385.

Weskamp N, Hüllermeier E, Kuhn D, Klebe G. Multiple graph aligment for the structural analysis of protein active sites. IEEE/ACM Transactions of Computational Biology and Bioinformatics. 2007;4(2):310-320.

Karimi I, Hüllermeier E. Risk assessment for natural hazards: A new approach based on fuzzy probability. Fuzzy Sets and Systems. 2007;158(9):987-999.

Hüllermeier E. Why fuzzy set theory is useful in data mining. In: Masseglia F, Poncelet P, Teisseire M, eds. Successes and New Directions in Data Mining. Information Science Reference, Hershey, New York; 2007:1-16.

Beringer J, Hüllermeier E. Fuzzy clustering of parallel data streams. In: Valente de Oliveira J, Pedrycz W, eds. Advances in Fuzzy Clustering and Its Applications . John Wiley and Sons; 2007:333-352.

Hüllermeier E. Fuzzy methods for data mining and machine learning: State of the art and prospects. In: Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Springer; 2007:355-374.

Hüllermeier E. Case-Based Approximate Reasoning. Vol 44. Springer; 2007.

Sulzmann JN, Fürnkranz J, Hüllermeier E. On pairwise Naive Bayes classifiers. In: In Proceedings ECML-2007, 17th European Conference on Machine Learning, Warsawa, Poland. Springer; 2007.

Beringer J, Hüllermeier E. Adaptive optimization of the number of clusters in fuzzy clustering, Dortmund, Germany 2006. In: Mikut R, Reischl M, eds. In Proceedings 16th Workshop Computational Intelligence, Dortmund Germany. ; 2006:140-149.

Hüllermeier E, Brinker K. Classification via fuzzy preference learning. In: Mikut R, Reischl M, eds. In Proceedings 16th Workshop Computational Intelligence, Dortmund Germany. ; 2006:190-199.

Hüllermeier E. Credible case-based inference using similarity profiles. In: Minor M, ed. In Proceedings of ECCBR-2006 Workshops, 8th European Conference on Case-Based Reasoning, Ölüdeniz/Fethiye Turkey. ; 2006:230-242.

Bade K, Hüllermeier E, Nürnberger A. Hierarchical classification by expected utility maximization. In: In Proceedings ICDM-2006, IEEE International Conference on Data Mining. IEEE Computer Society Press; 2006:43-52.

Brinker K, Hüllermeier E. Case-based label ranking. In: In Proceedings ECML-2006, 17th European Conference on Machine Learning, Berlin September 2006. Springer; 2006:566-573.

Brinker K, Fuernkranz J, Hüllermeier E. A unified model for multilabel classification and ranking. In: Brewka G, Coradeschi S, Perini A, Traverso P, eds. In Proceedings ECAI-2006, 17th European Conference on Artificial Inteligence, Riva Del Garda, Italy. ; 2006:489-493.

Hüllermeier E. Integrating instance-based learning and logistic regression. In: In Proceedings IPMU-2006 11th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Paris, France. ; 2006.

Hüllermeier E. The Choquet integral as an aggregation operator in case-based learning. In: Reusch B, ed. In Proceeding of the International Conference on Computational Intelligence, 9th Fuzzy Days, Dortmund. Springer; 2006:615-628.

Dubois D, Hüllermeier E, Prade H. A systematic approach to the assessment of fuzzy association rules. Data Mining and Knowledge Discovery. 2006;13(2):167-192.

Dubois D, Hüllermeier E, Prade H. Fuzzy methods in case-based recommendation and decision support. Journal of Intelligent Information Systems. 2006;27:95-115.

Hüllermeier E, Beringer J. Learning from ambiguously labeled examples. Intelligent Data Analysis. 2006;10(5):419-440.

Beringer J, Hüllermeier E. Online clustering of parallel data streams. Data and Knowledge Engineering. 2006;58(2):180-204.

Karimi I, Hüllermeier E, Meskouris K. A fuzzy-probablistic earthquake risk assessment system. Soft Computing. 2006;11(3):229-238.

Brinker K, Hüllermeier E. Calibrated label-ranking. In: Agarwal S, Cortes C, Herbrich R, eds. In Proceedings of the NIPS-2005 Workshop on Learning to Rank, Whistler, BC, Canada. http://web.mit.edu/shivani/www/Ranking-NIPS-05/.; 2005:1-6.

Hüllermeier E, Fürnkranz J, Beringer J. On position error and label ranking through iterated choice. In: In Proceedings LWA/FGML-2005, German Workshop on Machine Learning, Saarbrücken, Germany . ; 2005:158-163.