@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{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}},
}

@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}},
}