@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{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}}, } @article{1375, author = {{Beister, Frederic and Dräxler, Martin and Aelken, Jörg and Karl, Holger}}, issn = {{0140-3664}}, journal = {{Computer Communications}}, pages = {{77--85}}, publisher = {{Elsevier BV}}, title = {{{Power model design for ICT systems – A generic approach}}}, doi = {{10.1016/j.comcom.2014.02.007}}, volume = {{50}}, year = {{2014}}, } @inbook{46381, abstract = {{Exploratory Landscape Analysis is an effective and sophisticated approach to characterize the properties of continuous optimization problems. The overall aim is to exploit this knowledge to give recommendations of the individually best suited algorithm for unseen optimization problems. Recent research revealed a high potential of this methodology in this respect based on a set of well-defined, computable features which only requires a quite small sample of function evaluations. In this paper, new features based on the cell mapping concept are introduced and shown to improve the existing feature set in terms of predicting expert-designed high-level properties, such as the degree of multimodality or the global structure, for 2-dimensional single objective optimization problems.}}, author = {{Kerschke, Pascal and Preuss, Mike and Hernández, Carlos and Schütze, Oliver and Sun, Jian-Qiao and Grimme, Christian and Rudolph, Günter and Bischl, Bernd and Trautmann, Heike}}, booktitle = {{EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V}}, editor = {{Tantar, Alexandru-Adrian and Tantar, Emilia and Sun, Jian-Qiao and Zhang, Wei and Ding, Qian and Schütze, Oliver and Emmerich, Michael T M and Legrand, Pierrick and Del, Moral Pierre and Coello, Coello Carlos A}}, isbn = {{978-3-319-07493-1}}, pages = {{115–131}}, publisher = {{Springer International Publishing}}, title = {{{Cell Mapping Techniques for Exploratory Landscape Analysis}}}, doi = {{10.1007/978-3-319-07494-8_9}}, volume = {{288}}, year = {{2014}}, }