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

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

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

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

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

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

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

Dembczynski, K., Waegeman, W., Cheng, W., &; Hüllermeier, E. (2010). 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.

Cheng, W., Rademaker, M., De Beats, B., &; Hüllermeier, E. (2010). 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.

Hüllermeier, E. (2010). Uncertainty in clustering and classification . In in Proceedings SUM 2010, International Conference on Scalable Uncertainty Management  (Vol. 6379, pp. 16–19). Springer.

Cheng, W., &; Hüllermeier, E. (2009). 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 (pp. 28–38).

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

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

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

Fober, T., Mernberger, M., Melnikov, V., Moritz, R., &; Hüllermeier, E. (2009). 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 (pp. 30–36).

Fober, T., Klebe, G., &; Hüllermeier, E. (2009). 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 (pp. 1251–1256).

Boukhris, I., Elouedi, Z., Fober, T., Mernberger, M., &; Hüllermeier, E. (2009). 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 (pp. 1245–1250).

Fober, T., &; Hüllermeier, E. (2009). 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 (pp. 1299–1304).

Hüllermeier, E., &; Rifqi, M. (2009). 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 (pp. 1294–1298).

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

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

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

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

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

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

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

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

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

Fürnkranz, J., Hüllermeier, E., &; Vanderlooy, S. (2009). 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.

Cheng, W., &; Hüllermeier, E. (2009). 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 (pp. 707–716). Springer.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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