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

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

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

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

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

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

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

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

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

Uncertainty in clustering and classification 
E. Hüllermeier, in: In Proceedings SUM 2010, International Conference on Scalable Uncertainty Management , Springer, 2010, pp. 16–19.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

On the usefulness of fuzzy sets in data mining
E. Hüllermeier, in: R. Seising (Ed.), Views on Fuzzy Sets and Systems from Different Perspectives: Philosophy and Logic, Criticisms and Applications, Springer, 2009, pp. 457–470.

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

Binary decomposition methods for multipartite ranking
J. Fürnkranz, E. Hüllermeier, S. Vanderlooy, 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.

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

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

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

Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules
T. Fober, E. Hüllermeier, M. Mernberger, in: A. Beyer, M. Schroeder (Eds.), IIn Proceedings GCB-2008, Germany Conference on Bioinformatics, Dresden 2008, 2008, pp. 44–53.

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

Label ranking by learning pairwise preferences
E. Hüllermeier, J. Fürnkranz, W. Cheng, K. Brinker, Artificial Intelligence 172 (2008) 1897–1917.

A critical analysis of variants of the AUC
S. Vanderlooy, E. Hüllermeier, Machine Learning 72 (2008) 247–272.

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

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

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

Granular computing in machine learning and data mining
E. Hüllermeier, in: W. Pedrycz, A. Skowron, V. Kreinovich (Eds.), Handbook on Granular Computing, John Wiley and Sons, 2008, pp. 889–906.

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

Learning preference models from data: On the problem of label ranking and its variants
E. Hüllermeier, J. Fürnkranz, in: G. Della Riccia, D. Dubois, R. Kruse, H.J. Lenz (Eds.), Preferences and Similarities, Springer, 2008, pp. 283–304.

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

Learning similarity functions from qualitative feedback
W. Cheng, E. Hüllermeier, in: K.D. Althoff, R. Bergmann, M. Minor, A. Hanft (Eds.), In Proceedings ECCBR-2008 9th European Conference on Case-Based  Reasoning, Trier, Germany, Springer, 2008, pp. 120–134.

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

Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules
T. Fober, E. Hüllermeier, M. Mernberger, in: R. Mikut, M. Reischl (Eds.), In Proceedings 17th Workshop Computational Intelligence, Dortmund Germany, 2007, pp. 1–14.

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

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

Label ranking in case-based reasoning
K. Brinker, E. Hüllermeier, in: M. Richter, R. Weber (Eds.), In Proceedings ICCBR-2007, 7th International Conference on Case-Based Reasoning, Belfast, Northern Ireland, Springer, 2007, pp. 77–91.

Efficient instance-based learning on data streams
J. Beringer, E. Hüllermeier, in: P. Perner (Ed.), In Proceedings ICDM-2007, International Conference on Industrial Data Mining, Leipzig, Springer, 2007, pp. 34–48.

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

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

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

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

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

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

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

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

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

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

Why fuzzy set theory is useful in data mining
E. Hüllermeier, in: F. Masseglia, P. Poncelet, M. Teisseire (Eds.), Successes and New Directions in Data Mining, Information Science Reference, Hershey, New York, 2007, pp. 1–16.

Fuzzy clustering of parallel data streams
J. Beringer, E. Hüllermeier, in: J. Valente de Oliveira, W. Pedrycz (Eds.), Advances in Fuzzy Clustering and Its Applications , John Wiley and Sons, 2007, pp. 333–352.

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

Case-based Approximate Reasoning
E. Hüllermeier, Case-Based Approximate Reasoning, Springer, 2007.

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

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

Classification via fuzzy preference learning
E. Hüllermeier, K. Brinker, in: R. Mikut, M. Reischl (Eds.), In Proceedings 16th Workshop Computational Intelligence, Dortmund Germany, 2006, pp. 190–199.

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

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

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

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

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

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

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

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

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

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

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

Calibrated label-ranking
K. Brinker, E. Hüllermeier, in: S. Agarwal, C. Cortes, R. Herbrich (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, pp. 1–6.

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

A method for combining interacting pieces of evidence in case-based learning
E. Hüllermeier, C. NN, in: L. Kaelbling, A. Saffiotti (Eds.), In Proceedings IJCAI-05, 19th International Joint Conference on Artificial Intelligence, Edingburgh, Scotland, 2005, pp. 3–8.

Learning label preferences: Ranking error versus position error
E. Hüllermeier, J. Fürnkranz, in: A. Famili, J. Kok, J. Pena, A. Siebes, A. Feelders (Eds.), In Proceedings IDA-2005, 6th International Symposium on Intelligent Data Analysis, Madrid, Springer, 2005, pp. 180–191.

Learning from ambiguously labeled examples
E. Hüllermeier, J. Beringer, in: A. Famili, J. Kok, J. Pena, A. Siebes, A. Feelders (Eds.), In Proceedings IDA-2005, 6th International Symposium on Intelligent Data Analysis, Madrid, Springer, 2005, pp. 168–179.

A notion of comarative probabilistic entroy based on the possibilistic specificity ordering
D. Dubois, E. Hüllermeier, in: In Proceedings ECSQARU-2005, 8th European Conference of Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Barcelona, Spain, 2005.

A fuzzy-probablistic risk assessment system for natural disasters
I. Karimi, E. Hüllermeier, in: In Proceedings IFSA-2005, 11th  World Congress of the International Fuzzy System Association, Beijing, China, 2005, pp. 1147–1153.

Improving the interpretability of data-driven evolving fuzzy systems
E. Lughofer, E. Hüllermeier, E. Klement, in: In Proceedings EUSFLAT-2005 4th International Conference on the European Society for Fuzzy Logic and Technology, Barcelona, Spain, 2005.

Learning complexity-boundet rule-based classifiers by combining association analysis and genetic algorithms
Y. Yi, E. Hüllermeier, in: In Proceedings EUSFLAT-2005 4th International Conference on the European Society for Fuzzy Logic and Technology,  Barcelona, Spain, 2005.

Fuzzy sets in machine learning and data mining: Status and prospects
E. Hüllermeier, Fuzzy Sets and Systems 156 (2005) 387–406.

Experience-based decision making: A satisficing decision tree approach
E. Hüllermeier, IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans 35 (2005) 641–653.

Clustering of gene expression data using a local shape-based similarity measure
R. Balasubramaniyan, E. Hüllermeier, N. Weskamp, J. Kämper, Bioinformatics 21 (2005) 1069–1077.

A systematic approach to the assessment of fuzzy association rules
D. Dubois, E. Hüllermeier, H. Prade, in: J. Cubero, D. Sanchez, Z. Ras, T. Sudkamp (Eds.), In Proceedings of the Workshop on Alternative Techniques for Data Mining and Knowledge Discovery, IEEE International Conference on Data Mining, Brighton, UK, 2004.

Graph alignments: A new concept to detect conserved regions in protein active sites
N. Weskamp, E. Hüllermeier, D. Kuhn, G. Klebe, in: R. Giegerich, J. Stoye (Eds.), In Proceedings of GCB-2004 of the German Conference on Bioinformatics, Bielefeld, Germany, Springer, 2004, pp. 131–140.

Instance-based prediction with guaranteed confidence
E. Hüllermeier, in: R. Lopez de Mantaras, L. Saitta (Eds.), In Proceedings ECAI-2004, 16th European Conference on Artificial Inteligence, Valencia, Spain, IOS Press, 2004, pp. 97–101.

Comparison of ranking procedures in pairwise preference learning
E. Hüllermeier, J. Fürnkranz, in: In Proceedings IPMU-2004 10th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Perugia, Italy, 2004.

Ranking by pairwise comparison: A note on risk minimization
E. Hüllermeier, J. Fürnkranz, in: In Proceedings FUZZ-IEEE-04, IEEE International Conference on Fuzzy Systems, Budapest, Hungary, 2004.

An earthquake risk assessment method based on fuzzy probability
I. Karimi, E. Hüllermeier, K. Meskouris, in: In Proceedings 6th International FLINS Conference on Applied  Computational Intelligence, Duinse Polders, Blankenberg,Belgien, 2004.

Learning decision rules from positive and negative preferences
E. Hüllermeier, J. Beringer, in: In Proceedings IPMU-2004 10th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Perugia, Italy, 2004.

An evolutionary approach to constraints-regularized learning
E. Hüllermeier, I. Renners, A. Grauel, Mathware and Soft Computing 11 (2004) 109–124.

Efficient similarity search in protein structure databases: Improving clique-detection through clique-hashing
N. Weskamp, D. Kuhn, E. Hüllermeier, G. Klebe, Bioinformatics 20 (2004) 1522–1526.

Flexible constraints for regularization in learning from data
E. Hüllermeier, International Journal of Intelligent Systems 19 (2004) 525–541.