Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics - Melnikov, Vitaly; Hüllermeier, Eyke (Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017)

Learning TSK Fuzzy Rules from Data Streams - Shaker, Ammar; Heldt, W.; Hüllermeier, Eyke (Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia)

Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application in accounting - Bräuning, M.; Hüllermeier, Eyke; Keller, T.; Glaum, M. (European Journal of Operational Research)

Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic - Platenius, M.-C.; Shaker, Ammar; Becker, M.; Hüllermeier, Eyke; Schäfer, W. (IEEE Transactions on Software Engineering)

From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection - Hüllermeier, Eyke (The Computing Research Repository (CoRR))

Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem - Ramaswamy, Arunselvan; Bhatnagar, Shalabh (Stochastics)

Pairwise versus Pointwise Ranking: A Case Study - Melnikov, Vitalik; Hüllermeier, Eyke; Kaimann, Daniel; Frick, Bernd; Gupta, Pritha (Schedae Informaticae)

Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic - Platenius, Marie Christin; Shaker, Ammar; Becker, Matthias; Hüllermeier, Eyke; Schäfer, Wilhelm (IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017)
Today, software components are provided by global markets in the form of services. In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester’s requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user’s decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach.

Learning to Aggregate Using Uninorms - Melnikov, Vitaly; Hüllermeier, Eyke (Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016))
In this paper, we propose a framework for a class of learning problems that we refer to as “learning to aggregate”. Roughly, learning-to-aggregate problems are supervised machine learning problems, in which instances are represented in the form of a composition of a (variable) number on constituents; such compositions are associated with an evaluation, score, or label, which is the target of the prediction task, and which can presumably be modeled in the form of a suitable aggregation of the properties of its constituents. Our learning-to-aggregate framework establishes a close connection between machine learning and a branch of mathematics devoted to the systematic study of aggregation functions. We specifically focus on a class of functions called uninorms, which combine conjunctive and disjunctive modes of aggregation. Experimental results for a corresponding model are presented for a review data set, for which the aggregation problem consists of combining different reviewer opinions about a paper into an overall decision of acceptance or rejection.

Preference Learning - Fürnkranz, J.; Hüllermeier, Eyke (Encyclopedia of Machine Learning and Data Mining)

On the identifiability of models in multi-criteria preference learning - Labreuche, C.; Hüllermeier, Eyke; Vojtas, P.; Fallah Tehrani, A. (in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany)

Preference -based reinforcement learning using dyad ranking - Schäfer, D.; Hüllermeier, Eyke (in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany)

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators - Couso, Ines; Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke (in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany)

Support vector classification on noisy data using fuzzy superset losses - Lu, S.; Hüllermeier, Eyke (in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany)

Plackett-Luce networks for dyad ranking - Schäfer, D.; Hüllermeier, Eyke (in Workshop LWDA "Lernen, Wissen, Daten, Analysen" Potsdam, Germany)

Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts - Pfannschmidt, Karlson; Hüllermeier, Eyke; Held, S.; Neiger, R. (In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands)

CavSimBase: A database for large scale comparison of protein binding sites - Leinweber, M.; Fober, T.; Strickert, M.; Baumgärtner, L.; Klebe, G.; Freisleben, B.; Hüllermeier, Eyke (IEEE Transactions on Knowledge and Data Engineering)

Towards Automated Service Composition Under Quality Constraints - Mohr, Felix

Preference Learning - Fürnkranz, J.; Hüllermeier, Eyke (Encyclopedia of Machine Learning and Data Mining)

Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany