@inproceedings{457, abstract = {{Automatically composing service-based software solutionsis still a challenging task. Functional as well as nonfunctionalproperties have to be considered in order to satisfyindividual user requests. Regarding non-functional properties,the composition process can be modeled as optimization problemand solved accordingly. Functional properties, in turn, can bedescribed by means of a formal specification language. Statespacebased planning approaches can then be applied to solvethe underlying composition problem. However, depending on theexpressiveness of the applied formalism and the completenessof the functional descriptions, formally equivalent services maystill differ with respect to their implemented functionality. As aconsequence, the most appropriate solution for a desired functionalitycan hardly be determined without considering additionalinformation. In this paper, we demonstrate how to overcome thislack of information by means of Reinforcement Learning. Inorder to resolve ambiguity, we expand state-space based servicecomposition by a recommendation mechanism that supportsdecision-making beyond formal specifications. The recommendationmechanism adjusts its recommendation strategy basedon feedback from previous composition runs. Image processingserves as case study. Experimental results show the benefit of ourproposed solution.}}, author = {{Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd }}, booktitle = {{Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA)}}, pages = {{105--112}}, title = {{{Applying Reinforcement Learning for Resolving Ambiguity in Service Composition}}}, doi = {{10.1109/SOCA.2014.48}}, year = {{2014}}, } @inproceedings{428, abstract = {{Services are self-contained software components that can be used platform independent and that aim at maximizing software reuse. A basic concern in service oriented architectures is to measure the reusability of services. One of the most important qualities is the functional reusability, which indicates how relevant the task is that a service solves. Current metrics for functional reusability of software, however, either require source code analysis or have very little explanatory power. This paper gives a formally described vision statement for the estimation of functional reusability of services and sketches an exemplary reusability metric that is based on the service descriptions.}}, author = {{Mohr, Felix}}, booktitle = {{Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC)}}, pages = {{411--418}}, title = {{{Estimating Functional Reusability of Services}}}, year = {{2014}}, } @article{16046, author = {{Agarwal, M. and Fallah Tehrani, A. and Hüllermeier, Eyke}}, journal = {{Journal of Multi-Criteria Decision Analysis}}, number = {{3-4}}, title = {{{Preference-based learning of ideal solutions in TOPSIS-like decision models}}}, volume = {{22}}, year = {{2014}}, } @article{16060, author = {{Krotzky, T. and Fober, T. and Hüllermeier, Eyke and Klebe, G.}}, journal = {{IEEE/ACM Transactions of Computational Biology and Bioinformatics}}, number = {{5}}, pages = {{878--890}}, title = {{{Extended graph-based models for enhanced similarity search in Cabase}}}, volume = {{11}}, year = {{2014}}, } @article{16064, author = {{Hüllermeier, Eyke}}, journal = {{International Journal of Approximate Reasoning}}, number = {{7}}, pages = {{1519--1534}}, title = {{{Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization}}}, volume = {{55}}, year = {{2014}}, } @article{16069, author = {{Henzgen, Sascha and Strickert, M. and Hüllermeier, Eyke}}, journal = {{Evolving Systems}}, pages = {{175--191}}, title = {{{Visualization of evolving fuzzy-rule-based systems}}}, volume = {{5}}, year = {{2014}}, } @article{16077, author = {{Busa-Fekete, Robert and Szörenyi, 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{16078, author = {{Krempl, G. and Zliobaite, I. and Brzezinski, D. and Hüllermeier, Eyke and Last, M. and Lemaire, V. and Noack, T. and Shaker, A. 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{16079, author = {{Strickert, M. and Bunte, K. and Schleif, F.M. and Hüllermeier, Eyke}}, journal = {{Neurocomputing}}, pages = {{97--109}}, title = {{{Correlation-based embedding of pairwise score data}}}, volume = {{141}}, year = {{2014}}, } @article{16080, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, journal = {{International Journal of Applied Mathematics and Computer Science}}, number = {{1}}, pages = {{199--212}}, title = {{{Survival analysis on data streams: Analyzing temporal events in dynamically changing environments}}}, volume = {{24}}, year = {{2014}}, } @article{16082, author = {{Senge, Robin and Bösner, S. and Dembczynski, K. and Haasenritter, J. and Hirsch, O. and Donner-Banzhoff, N. and Hüllermeier, Eyke}}, journal = {{Information Sciences}}, pages = {{16--29}}, title = {{{Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty}}}, volume = {{255}}, year = {{2014}}, } @article{16083, author = {{Donner-Banzhoff, N. and Haasenritter, J. and Hüllermeier, Eyke and Viniol, A. and Bösner, S. and Becker, A.}}, journal = {{Journal of Clinical Epidemiology}}, number = {{67}}, pages = {{124--132}}, title = {{{The comprehensive diagnostic study is suggested as a design to model the diagnostic process}}}, volume = {{2}}, year = {{2014}}, } @inproceedings{10247, author = {{Busa-Fekete, Robert and Szörényi, B. and Hüllermeier, Eyke}}, booktitle = {{Proceedings AAAI 2014, Quebec, Canada}}, pages = {{1701--1707}}, title = {{{PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences}}}, year = {{2014}}, } @inproceedings{10248, author = {{Busa-Fekete, Robert and Hüllermeier, Eyke}}, booktitle = {{Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia}}, pages = {{18--39}}, title = {{{A Survey of Preference-Based Online Learning with Bandit Algorithms}}}, year = {{2014}}, } @inproceedings{10249, author = {{Henzgen, Sascha and Hüllermeier, Eyke}}, booktitle = {{Proceedings Discovery Science, Bled,Slovenia }}, pages = {{123--134}}, title = {{{Mining Rank Data}}}, year = {{2014}}, } @inproceedings{10250, author = {{Fallah Tehrani, A. and Strickert, M. and Hüllermeier, Eyke}}, booktitle = {{Proceedings ESANN , Bruges, Belgium}}, title = {{{The Choquet kernel for monotone data}}}, year = {{2014}}, } @inproceedings{10251, author = {{Abdel-Aziz, A. and Strickert, M. and Hüllermeier, Eyke}}, booktitle = {{Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland}}, pages = {{17--31}}, title = {{{Learning Solution Similarity in Preference-Based CBR}}}, year = {{2014}}, } @inproceedings{10253, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany}}, pages = {{32--33}}, title = {{{Dyad Ranking Using A Bilinear Plackett-Luce Model}}}, year = {{2014}}, } @inproceedings{10254, author = {{Calders, T. and Esposito, F. and Hüllermeier, Eyke and Meo, R.}}, booktitle = {{Proceedings, Parts I-III. Lecture Notes in Computer Science}}, pages = {{8724--8726}}, publisher = {{Springer}}, title = {{{Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France}}}, year = {{2014}}, } @inproceedings{10295, author = {{Fürnkranz, J. and Hüllermeier, Eyke and Rudin, Cynthia and Slowinski, Roman and Sanner, Scott}}, number = {{3}}, pages = {{1--27}}, title = {{{Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports}}}, volume = {{4}}, year = {{2014}}, } @article{10296, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, journal = {{Applied Mathematics and Computer Science}}, number = {{1}}, pages = {{199--212}}, title = {{{Survival analysis on data streams: Analyzing temporal events in dynamically changing environments}}}, volume = {{24}}, year = {{2014}}, } @article{10297, author = {{Hoffmann, F. and Hüllermeier, Eyke and Kroll, A.}}, journal = {{Computational Intelligence Automatisierungstechnik}}, number = {{10}}, pages = {{685--686}}, title = {{{Ausgewählte Beiträge des GMA-Fachausschusses 5.14}}}, volume = {{62}}, year = {{2014}}, } @article{10298, author = {{Calders, T. and Esposito, F. and Hüllermeier, Eyke and Meo, R.}}, journal = {{Data Min. Knowledge Discovery}}, number = {{5-6}}, pages = {{1129--1133}}, title = {{{Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track}}}, volume = {{28}}, year = {{2014}}, } @article{10299, author = {{Henzgen, Sascha and Strickert, M. and Hüllermeier, Eyke}}, journal = {{Evolving Systems}}, number = {{3}}, pages = {{175--191}}, title = {{{Visualization of evolving fuzzy rule-based systems}}}, volume = {{5}}, year = {{2014}}, } @article{10308, author = {{Hüllermeier, Eyke}}, journal = {{Int. J. Approx. Reasoning}}, number = {{7}}, pages = {{1519--1534}}, title = {{{Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization}}}, volume = {{55}}, year = {{2014}}, } @article{10309, author = {{Hüllermeier, Eyke}}, journal = {{Int. J. Approx. Reasoning}}, number = {{7}}, pages = {{1609--1613}}, title = {{{Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization}}}, volume = {{55}}, year = {{2014}}, } @article{10310, author = {{Strickert, M. and Bunte, K. and Schleif, F.- M. and Hüllermeier, Eyke}}, journal = {{Neurocomputing}}, pages = {{97--109}}, title = {{{Correlation-based embedding of pairwise score data}}}, volume = {{141}}, year = {{2014}}, } @article{10311, author = {{Senge, Robin and Bösner, S. and Dembczynski, K. and Haasenritter, J. and Hirsch, O. and Donner-Banzhoff, N. and Hüllermeier, Eyke}}, journal = {{Information Sciences}}, pages = {{16--29}}, title = {{{Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty}}}, volume = {{255}}, year = {{2014}}, } @article{10312, author = {{Mernberger, M. and Moog, M. and Stork, S. and Zauner, S. and Maier, U.G. and Hüllermeier, Eyke}}, journal = {{J. Bioinformatics and Computational Biology}}, number = {{1}}, title = {{{Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances}}}, volume = {{12}}, year = {{2014}}, } @article{10313, author = {{Calders, T. and Esposito, F. and Hüllermeier, Eyke and Meo, R.}}, journal = {{Machine Learning}}, number = {{1-2}}, pages = {{1--3}}, title = {{{Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track}}}, volume = {{97}}, year = {{2014}}, } @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}}, }