Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., &; Hüllermeier, E. (2015). On the Bayes-Optimality of F-Measure Maximizers. In Journal of Machine Learning Research, 15, 3333–3388.

Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.

Shaker, A., &; Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.

Hüllermeier, E. (2015). From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.

Garcia-Jimenez, S., Bustince, U., Hüllermeier, E., Mesiar, R., Pal, N. R., &; Pradera, A. (2015). Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 23(4), 1259–1273.

Senge, R., &; Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.

Basavaraju, M., Chandran, L. S., Rajendraprasad, D., &; Ramaswamy, A. (2014). Rainbow connection number of graph power and graph products. Graphs and Combinatorics, 30(6), 1363–1382.

Basavaraju, M., Chandran, L. S., Rajendraprasad, D., &; Ramaswamy, A. (2014). Rainbow connection number and radius. Graphs and Combinatorics, 30(2), 275–285.

Mohr, F., &; Walther, S. (2014). Template-based Generation of Semantic Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 188–203).https://doi.org/10.1007/978-3-319-14130-5_14 

Jungmann, A., Mohr, F., &; Kleinjohann, B. (2014). Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. In Proceedings of the 10th World Congress on Services (SERVICES) (pp. 346–353).https://doi.org/10.1109/SERVICES.2014.68 

Jungmann, A., Mohr, F., &; Kleinjohann, B. (2014). Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. In Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA) (pp. 105–112).https://doi.org/10.1109/SOCA.2014.48 

Mohr, F. (2014). Estimating Functional Reusability of Services. In Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC) (pp. 411–418).

Agarwal, M., Fallah Tehrani, A., &; Hüllermeier, E. (2014). Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis, 22(3–4).

Krotzky, T., Fober, T., Hüllermeier, E., &; Klebe, G. (2014). Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 11(5), 878–890.

Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning, 55(7), 1519–1534.

Henzgen, S., Strickert, M., &; Hüllermeier, E. (2014). Visualization of evolving fuzzy-rule-based systems. Evolving Systems, 5, 175–191.

Busa-Fekete, R., Szörenyi, B., Weng, P., Cheng, W., &; Hüllermeier, E. (2014). Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.

Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V.,  Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.

Strickert, M., Bunte, K., Schleif, F. M., &; Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.

Shaker, A., &; Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science, 24(1), 199–212.