@article{10314, author = {{Busa-Fekete, Robert and Szörényi, B. and Weng, P. and Cheng, W. and Hüllermeier, Eyke}}, journal = {{Machine Learning}}, number = {{3}}, pages = {{327--351}}, title = {{{Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm}}}, volume = {{97}}, year = {{2014}}, } @article{10315, author = {{Montanés, E. and Senge, Robin and Barranquero, J. and Quevedo, J.R. and Del Coz, J.J. and Hüllermeier, Eyke}}, journal = {{Pattern Recognition}}, number = {{3}}, pages = {{1494--1508}}, title = {{{Dependent binary relevance models for multi-label classification}}}, volume = {{47}}, year = {{2014}}, } @article{10316, author = {{Krempl, G. and Zliobaite, I. and Brzezinski, D. and Hüllermeier, Eyke and Last, M. and Lemaire, V. and Noack, T. and Shaker, Ammar and Sievi, S. and Spiliopoulou, M. and Stefanowski, J.}}, journal = {{SIGKDD Explorations}}, number = {{1}}, pages = {{1--10}}, title = {{{Open challenges for data stream mining research}}}, volume = {{16}}, year = {{2014}}, } @article{10317, author = {{Krotzky, T. and Fober, T. and Hüllermeier, Eyke and Klebe, G.}}, journal = {{IEEE/ACM Trans. Comput. Biology Bioinform.}}, number = {{5}}, pages = {{878--890}}, title = {{{Extended Graph-Based Models for Enhanced Similarity Search in Cavbase}}}, volume = {{11}}, year = {{2014}}, } @article{10318, author = {{Stock, M. and Fober, T. and Hüllermeier, Eyke and Glinca, S, and Klebe, G. and Pahikkala, T. and Airola, A. and De Baets, B. and Wageman, W.}}, journal = {{IEEE/ACM Trans. Comput. Biology Bioinform.}}, number = {{6}}, pages = {{1157--1169}}, title = {{{Identification of Functionally Releated Enzymes by Learning to Rank Methods}}}, volume = {{11}}, year = {{2014}}, } @inproceedings{485, abstract = {{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.}}, author = {{Mohr, Felix and Kleine Büning, Hans}}, booktitle = {{Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS)}}, pages = {{676--680}}, title = {{{Semi-Automated Software Composition Through Generated Components}}}, doi = {{10.1145/2539150.2539235}}, year = {{2013}}, } @inproceedings{495, abstract = {{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.}}, author = {{Mohr, Felix and Lettmann, Theodor and Kleine Büning, Hans}}, booktitle = {{Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA)}}, pages = {{154--161}}, title = {{{Reducing Nondeterminism in Automated Service Composition}}}, doi = {{10.1109/SOCA.2013.25}}, year = {{2013}}, } @inproceedings{15752, author = {{Cheng, W. and Henzgen, S. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany}}, pages = {{129--136}}, title = {{{Labelwise versus pairwise decomposition in label ranking}}}, year = {{2013}}, } @inproceedings{15753, author = {{Senge, Robin and del Coz, J. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany}}, pages = {{151--158}}, title = {{{Rectifying classifier chains for multi-label classification, Bamberg, Germany}}}, year = {{2013}}, } @inproceedings{15755, author = {{Busa-Fekete, Robert and Fober, T. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke}}, pages = {{237--246}}, publisher = {{KIT Scientific Publishing}}, title = {{{Preference-based evolutionary optimization using generalized racing algorithms}}}, year = {{2013}}, } @inproceedings{15756, author = {{Henzgen, S. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke}}, pages = {{227--236}}, publisher = {{KIT Scientific Publishing}}, title = {{{Weighted rank correlation measures based on fuzzy order relations}}}, year = {{2013}}, } @inproceedings{15757, author = {{Weng, P. and Busa-Fekete, Robert and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague}}, title = {{{Interactive Q-learning with ordinal rewards and unreliable tutor}}}, year = {{2013}}, } @inproceedings{15758, author = {{Busa-Fekete, Robert and Szörenyi, B. and Weng, P. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague}}, title = {{{Preference-based evolutionary direct policy search}}}, year = {{2013}}, } @inproceedings{15759, author = {{Cheng, W. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings M-PREF`13, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China}}, title = {{{A nearest neigbor approach to label ranking based on generalized labelwise loss minimization}}}, year = {{2013}}, } @inproceedings{15760, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, booktitle = {{In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic}}, editor = {{Krempl, G. and Zliobaite, I. and Wang, Y. and Forman, G.}}, pages = {{38--41}}, title = {{{Event history analysis on data streams: An application to earthquake occurence}}}, year = {{2013}}, } @inproceedings{15761, author = {{Senge, Robin and del Coz, J.J. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany }}, editor = {{Schmidt-Thieme, L. and Spiliopoulou, M.}}, publisher = {{Springer}}, title = {{{On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery}}}, year = {{2013}}, } @inproceedings{15763, author = {{Fober, T. and Klebe, G. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany}}, editor = {{Lausen, B. and Van den Poel, D. and Ultsch, A.}}, pages = {{279--286}}, publisher = {{Springer}}, title = {{{Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics, Algorithms from and for Nature and Life}}}, year = {{2013}}, } @inproceedings{15112, author = {{Fallah Tehrani, A. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy}}, editor = {{Montero, J. and Pasi, G. and Ciucci, D.}}, publisher = {{Atlantis Press}}, title = {{{Ordinal Choquistic regression }}}, year = {{2013}}, } @inproceedings{15113, author = {{Nasiri, N. and Fober, T. and Senge, Robin and Hüllermeier, Eyke}}, booktitle = {{in Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada}}, pages = {{715--721}}, title = {{{Fuzzy Pattern Trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of colour yield in poyester dyeing, Edmonton, Canada}}}, year = {{2013}}, } @article{16044, author = {{Heider, D. and Senge, Robin and Cheng, W. and Hüllermeier, Eyke}}, journal = {{Bioinformatics}}, number = {{16}}, pages = {{1946--1952}}, title = {{{Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction}}}, volume = {{29}}, year = {{2013}}, }