TY - JOUR AU - Busa-Fekete, Robert AU - Szörényi, B. AU - Weng, P. AU - Cheng, W. AU - Hüllermeier, Eyke ID - 10314 IS - 3 JF - Machine Learning TI - Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm VL - 97 ER - TY - JOUR AU - Montanés, E. AU - Senge, Robin AU - Barranquero, J. AU - Quevedo, J.R. AU - Del Coz, J.J. AU - Hüllermeier, Eyke ID - 10315 IS - 3 JF - Pattern Recognition TI - Dependent binary relevance models for multi-label classification VL - 47 ER - TY - JOUR AU - Krempl, G. AU - Zliobaite, I. AU - Brzezinski, D. AU - Hüllermeier, Eyke AU - Last, M. AU - Lemaire, V. AU - Noack, T. AU - Shaker, Ammar AU - Sievi, S. AU - Spiliopoulou, M. AU - Stefanowski, J. ID - 10316 IS - 1 JF - SIGKDD Explorations TI - Open challenges for data stream mining research VL - 16 ER - TY - JOUR AU - Krotzky, T. AU - Fober, T. AU - Hüllermeier, Eyke AU - Klebe, G. ID - 10317 IS - 5 JF - IEEE/ACM Trans. Comput. Biology Bioinform. TI - Extended Graph-Based Models for Enhanced Similarity Search in Cavbase VL - 11 ER - TY - JOUR AU - Stock, M. AU - Fober, T. AU - Hüllermeier, Eyke AU - Glinca, S, AU - Klebe, G. AU - Pahikkala, T. AU - Airola, A. AU - De Baets, B. AU - Wageman, W. ID - 10318 IS - 6 JF - IEEE/ACM Trans. Comput. Biology Bioinform. TI - Identification of Functionally Releated Enzymes by Learning to Rank Methods VL - 11 ER - TY - CONF AB - Software composition has been studied as a subject of state based planning for decades. Existing composition approaches that are efficient enough to be used in practice are limited to sequential arrangements of software components. This restriction dramatically reduces the number of composition problems that can be solved. However, there are many composition problems that could be solved by existing approaches if they had a possibility to combine components in very simple non-sequential ways. To this end, we present an approach that arranges not only basic components but also composite components. Composite components enhance the structure of the composition by conditional control flows. Through algorithms that are written by experts, composite components are automatically generated before the composition process starts. Therefore, our approach is not a substitute for existing composition algorithms but complements them with a preprocessing step. We verified the validity of our approach through implementation of the presented algorithms. AU - Mohr, Felix AU - Kleine Büning, Hans ID - 485 T2 - Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS) TI - Semi-Automated Software Composition Through Generated Components ER - TY - CONF AB - Automated service composition has been studied as a subject of state based planning for a decade. A great deal of service composition tasks can only be solved if concrete output values of the services are considered in the composition process. However, the fact that those values are not known before runtime leads to nondeterministic planning problems, which have proven to be notoriously difficult in practical automated service composition applications. Even though this problem is frequently recognized, it has still received remarkably few attention and remains unsolved.This paper shows how nondeterminism in automated service composition can be reduced. We introduce context rules as a means to derive semantic knowledge from output values of services. These rules enable us to replace nondeterministic composition operations by less nondeterministic or even completely deterministic ones. We show the validity of our solutions not only theoretically but also have evaluated them practically through implementation. AU - Mohr, Felix AU - Lettmann, Theodor AU - Kleine Büning, Hans ID - 495 T2 - Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA) TI - Reducing Nondeterminism in Automated Service Composition ER - TY - CONF AU - Cheng, W. AU - Henzgen, S. AU - Hüllermeier, Eyke ID - 15752 T2 - In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany TI - Labelwise versus pairwise decomposition in label ranking ER - TY - CONF AU - Senge, Robin AU - del Coz, J. AU - Hüllermeier, Eyke ID - 15753 T2 - In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany TI - Rectifying classifier chains for multi-label classification, Bamberg, Germany ER - TY - CONF AU - Busa-Fekete, Robert AU - Fober, T. AU - Hüllermeier, Eyke ED - Hoffmann, F. ED - Hüllermeier, Eyke ID - 15755 T2 - in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany TI - Preference-based evolutionary optimization using generalized racing algorithms ER -