Bösner, S., Bönisch, K., Haasenritter , J., Schlegel, P., Hüllermeier, E., &; Donner-Banzhoff, N. (2013). Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. . BMC Family Practice, 14(1), 154–162.

Haasenritter, J., Viniol, A., Becker, A., Bösner, S., Hüllermeier, E., Senge, R., &; Donner-Banzhoff, N. (2013). Diagnose im Kontext - eine erweiterte Perspektive. Zeitschrift Für Evidenz, Fortbildung Und Qualität Im Gesundheitswesen (ZEFQ), 107, 585–591.

Shaker, A., Senge, R., &; Hüllermeier, E. (2013). Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences, 220, 34–45.

Szarvas, G., Busa-Fekete, R., &; Hüllermeier, E. (2013). Learning to rank lexical substitutions. In In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA.

Dembczynski, K., Jachnik, A., Kotlowski, W., Waegeman, W., &; Hüllermeier, E. (2013). Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization. In S. Dasgupta &; D. McAllester (Eds.), in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA (pp. 1130–1138).

Busa-Fekete, R., Szoreny, B., Weng, P., Cheng, W., &; Hüllermeier, E. (2013). Top-k selection based on adaptive sampling of noisy preferences. In S. Dasgupta &; D. McAllester (Eds.), in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA (pp. 1094–1102).

Hüllermeier, E., &; Cheng, W. (2013). Preference-based CBR: General ideas and basic principles. In F. Rossi (Ed.), in Proceedings IJCAI-13, 23rd international Joint Conference on Artificial Intelligence, Beijing, China (pp. 3012–3016). AAAI Press.

Henzgen, S., Strickert, M., &; Hüllermeier, E. (2013). Rule chains for visualizing evolving fuzzy rule-based systems. In R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, &; A. Zolnierek (Eds.), in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland (pp. 279–288). Springer.

Shaker, A., &; Hüllermeier, E. (2013). Recovery analysis for adaptive learning from non-stationary data streams. In R. Burduk, K. Jackowski, M. Kurzynski, W. Wozniak, &; A. Zolnierek (Eds.), in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland (pp. 289–298). Springer.

Leinweber, M., Baumgärtner, L., Mernberger, M., Fober, T., Hüllermeier, E., Klebe, G., &; Freisleben, B. (2012). GPU-based cloud computing for comparing the structure of protein binding sites. In in Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione d`Italia, Italy.

Hüllermeier, E., &; Fallah Tehrani, A. (2012). Efficient learning of classifiers based on the 2-additive Choquet integral. In C. Moewes &; A. Nürnberger (Eds.), Computational Intelligence in Intelligent Data Analysis (pp. 17–30). Springer.

Bräuning, M., &; Hüllermeier, E. (2012). Learning conditional lexicographic preference trees. In In Workshops on Preference Learning at ECAI, European Conference on Artiticial intelligence, Montpellier, France.

Hüllermeier, E., &; Fallah Tehrani, A. (2012). On the VC dimension of the Choquet integral. In In Proceedings IPMU-2012 14th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Catania, Italy.

Fallah Tehrani, A., Cheng, W., Dembczynski, K., &; Hüllermeier, E. (2012). Learning  monotone nonlinear models using the Choquet integral. Machine Learning, 89(1), 183–211.

Bustince, H., Pagola, M., Mesiar, R., Hüllermeier, E., &; Herrera, F. (2012). Grouping, overlap and generalized bientropic functions for fuzzy modeling of pairwise comparisons. IEEE Transactions on Fuzzy Systems, 20(3), 405–415.

Fürnkranz, J., Hüllermeier, E., Cheng, W., &; Park, S. H. (2012). Preference-based reinforcement learning: A formal framework and a policy iteration algorithm. Machine Learning, 89(1), 123–156.

Dembczynski, K., Waegeman, W., Cheng, W., &; Hüllermeier, E. (2012). On label dependence and loss  minimization in multi-label classification. Machine Learning, 88(1–2), 5–45.

Shaker, A., &; Hüllermeier, E. (2012). IBL Streams: A system for instance-based classification and regression on data streams. Evolving Systems, 3(4), 235–249.

Dolorez Ruiz, M., &; Hüllermeier, E. (2012). A formal and empirical analysis of the fuzzy gamma rank correlation coefficient. Information Sciences, 206, 1–17.

Fallah Tehrani, A., Cheng, W., &; Hüllermeier, E. (2012). Preference learning using the Choquet integral: The case of multipartite ranking. IEEE Transactions on Fuzzy Systems, 20(6), 1102–1113.