Jasinska, K., Dembczynski, K., Busa-Fekete, R., Klerx, T., &; Hüllermeier, E. (2016). Extreme F-measure maximization using sparse probability estimates . In M. F. Balcan &; K. Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA.
LibreCat: /record/10222

Melnikov, V., &; Hüllermeier, E. (2016). Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016. In European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 756–771).
LibreCat: /record/10223

Dembczynski, K., Kotlowski, W., Waegeman, W., Busa-Fekete, R., &; Hüllermeier, E. (2016). Consistency of probalistic classifier trees. In In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 511–526).
LibreCat: /record/10224

Shabani, A., Paul, A., Platon, R., &; Hüllermeier, E. (2016). Predicting the electricity consumption of buildings: An improved CBR approach. In In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA (pp. 356–369).
LibreCat: /record/10225

Pfannschmidt, K., Hüllermeier, E., Held, S., &; Neiger, R. (2016). Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
LibreCat: /record/10226

Labreuche, C., Hüllermeier, E., Vojtas, P., &; Fallah Tehrani, A. (2016). On the Identifiability of models in multi-criteria preference learning . In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
LibreCat: /record/10227

Schäfer, D., &; Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
LibreCat: /record/10228

Couso, I., Ahmadi Fahandar, M., &; Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
LibreCat: /record/10229

Lu, S., &; Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy supersets losses. In F. Hoffmann, E. Hüllermeier, &; R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing (pp. 1–8).
LibreCat: /record/10230

Schäfer, D., &; Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In In Workshop LWDA “Lernen, Wissen, Daten, Analysen.”
LibreCat: /record/10231

Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F., &; van Harmelen, F. (Eds.). (2016). ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence (Vol. 285). The Hague, The Netherlands: IOS Press.
LibreCat: /record/10263

Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., &; Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
LibreCat: /record/10264

Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., &; Heider, D. (2016). Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining, 9(10).
LibreCat: /record/10266

Arifulina, S., Platenius, M. C., Mohr, F., Engels, G., &; Schäfer, W. (2015). Market-Specific Service Compositions: Specification and Matching. In Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet (pp. 333--340).https://doi.org/10.1109/SERVICES.2015.58 
LibreCat: /record/280
DOI: 10.1109/SERVICES.2015.58

Jungmann, A., &; Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications, (1), 1–18.https://doi.org/10.1186/s13174-015-0022-8 
LibreCat: /record/323
DOI: 10.1186/s13174-015-0022-8

Mohr, F. (2015). A Metric for Functional Reusability of Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 298--313).https://doi.org/10.1007/978-3-319-14130-5_21 
LibreCat: /record/324
DOI: 10.1007/978-3-319-14130-5_21

Mohr, F., Jungmann, A., &; Kleine Büning, H. (2015). Automated Online Service Composition. In Proceedings of the 12th IEEE International Conference on Services Computing (SCC) (pp. 57--64).https://doi.org/10.1109/SCC.2015.18 
LibreCat: /record/319
DOI: 10.1109/SCC.2015.18

Senge, R., &; Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.https://doi.org/10.1109/tfuzz.2015.2396078 
LibreCat: /record/4792
DOI: 10.1109/tfuzz.2015.2396078

Schäfer, D., &; Hüllermeier, E. (2015). Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal (pp. 110–111).
LibreCat: /record/15406

Paul, A., &; Hüllermeier, E. (2015). A cbr approach to the angry birds game. In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany (pp. 68–77).
LibreCat: /record/15749