J. Hühn and E. Hüllermeier, “FURIA: An Algorithm for unordered fuzzy rule induction,” Data Mining and Knowledge Discovery , vol. 19, pp. 293–319, 2009.
LibreCat: /record/16158

E. Hüllermeier and S. Vanderlooy, “Why fuzzy decision trees are good rankers,” IEEE Transactions on Fuzzy Systems, vol. 17, no. 6, pp. 1233–1244, 2009.
LibreCat: /record/16159

Y. Yi, T. Fober, and E. Hüllermeier, “Fuzzy operator trees for modeling rating functions,” International Journal of Computational Intelligence and Applications, vol. 8, no. 4, pp. 413–428, 2009.
LibreCat: /record/16161

T. Fober, M. Mernberger, G. Klebe, and E. Hüllermeier, “Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules,” Bioinformatics, vol. 25, no. 16, pp. 2110–2117, 2009.
LibreCat: /record/16162

J. Hühn and E. Hüllermeier, “F: A fuzzy rule learner for inducing reliable classifiers,” IEEE Transactions on Fuzzy Systems, vol. 17, no. 1, pp. 138–149, 2009.
LibreCat: /record/16163

E. Hüllermeier, I. Vladimirskiy, B. Prados Suarez, and E. Stauch, “Supporting case-based retrieval by similarity skylines: Basic concepts and extensions,” Künstliche Intelligenz, vol. 1, no. 09, pp. 24–29, 2009.
LibreCat: /record/16165

E. Hüllermeier, “On the usefulness of fuzzy sets in data mining,” in Views on Fuzzy Sets and Systems from Different Perspectives: Philosophy and Logic, Criticisms and Applications, R. Seising, Ed. Springer, 2009, pp. 457–470.
LibreCat: /record/10188

W. Cheng, J. Hühn, and E. Hüllermeier, “Decision tree and instance-based learning for label ranking,” in in Proceedings ICML-2009, 26th International Conference on Machine Learning, Montreal, Canada, 2009, pp. 161–168.
LibreCat: /record/13595

J. Fürnkranz, E. Hüllermeier, and S. Vanderlooy, “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.
LibreCat: /record/13596

W. Cheng and E. Hüllermeier, “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, 2009, no. LNCS 5551, pp. 707–716.
LibreCat: /record/13598

E. Hüllermeier and S. Vanderlooy, “Weighted voting as approximate MAP prediction in pairwise classification,” in In Proceedings Workshop LWA-2008, Lernen-Wissensentdeckung-Adaptivität, Würzburg, Germany, 2008, pp. 34–41.
LibreCat: /record/15779

T. Fober, E. Hüllermeier, and M. Mernberger, “Evolutionary construction of multiple graph alignments for mining structured biomolecular data ,” in In Proceedings Workshop LWA-2008, Lernen-Wissensentdeckung-Adaptivität, Würzburg, Germany, 2008, pp. 27–33.
LibreCat: /record/15780

T. Fober, E. Hüllermeier, and M. Mernberger, “Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules,” in iIn Proceedings GCB-2008, Germany Conference on Bioinformatics, Dresden 2008, 2008, pp. 44–53.
LibreCat: /record/15781

J. Hühn and E. Hüllermeier, “Is an ordinal class structure useful in classifier learning?,” International Journal of Data Mining, Modeling and Management , vol. 1, no. 1, pp. 45–67, 2008.
LibreCat: /record/16164

E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker, “Label ranking by learning pairwise preferences,” Artificial Intelligence, vol. 172, pp. 1897–1917, 2008.
LibreCat: /record/16166

S. Vanderlooy and E. Hüllermeier, “A critical analysis of variants of the AUC,” Machine Learning, vol. 72, pp. 247–272, 2008.
LibreCat: /record/16167

J. Fürnkranz, E. Hüllermeier, E. Mencia, and K. Brinker, “Multilabel classification via calibrated label ranking,” Machine Learning, vol. 73, no. 2, pp. 133–153, 2008.
LibreCat: /record/16168

E. Hüllermeier and K. Brinker, “Learning valued preference structures for solving classification problems,” Fuzzy Sets and Systems, vol. 159, no. 18, pp. 2337–2352, 2008.
LibreCat: /record/16169

J. Beringer and E. Hüllermeier, “Case-based learning in a bipolar possibilistic framework,” International Journal of Intelligent Systems, vol. 23, no. 10, pp. 1119–1134, 2008.
LibreCat: /record/16184

E. Hüllermeier, “Granular computing in machine learning and data mining,” in Handbook on Granular Computing, W. Pedrycz, A. Skowron, and V. Kreinovich, Eds. John Wiley and Sons, 2008, pp. 889–906.
LibreCat: /record/16225