Survival analysis on data streams: Analyzing temporal events in dynamically changing environments
A. Shaker, E. Hüllermeier, Applied Mathematics and Computer Science 24 (2014) 199–212.

Ausgewählte Beiträge des GMA-Fachausschusses 5.14
F. Hoffmann, E. Hüllermeier, A. Kroll, Computational Intelligence Automatisierungstechnik 62 (2014) 685–686.

Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track
T. Calders, F. Esposito, E. Hüllermeier, R. Meo, Data Min. Knowledge Discovery 28 (2014) 1129–1133.

Visualization of evolving fuzzy rule-based systems
S. Henzgen, M. Strickert, E. Hüllermeier, Evolving Systems 5 (2014) 175–191.

Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
E. Hüllermeier, Int. J. Approx. Reasoning 55 (2014) 1519–1534.

Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
E. Hüllermeier, Int. J. Approx. Reasoning 55 (2014) 1609–1613.

Correlation-based embedding of pairwise score data
M. Strickert, K. Bunte, F.-M. Schleif, E. Hüllermeier, Neurocomputing 141 (2014) 97–109.

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty
R. Senge, S. Bösner, K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff, E. Hüllermeier, Information Sciences 255 (2014) 16–29.

Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances
M. Mernberger, M. Moog, S. Stork, S. Zauner, U.G. Maier, E. Hüllermeier, J. Bioinformatics and Computational Biology 12 (2014).

Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track
T. Calders, F. Esposito, E. Hüllermeier, R. Meo, Machine Learning 97 (2014) 1–3.

Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm
R. Busa-Fekete, B. Szörényi, P. Weng, W. Cheng, E. Hüllermeier, Machine Learning 97 (2014) 327–351.

Dependent binary relevance models for multi-label classification
E. Montanés, R. Senge, J. Barranquero, J.R. Quevedo, J.J. Del Coz, E. Hüllermeier, Pattern Recognition 47 (2014) 1494–1508.

Open challenges for data stream mining research
G. Krempl, I. Zliobaite, D. Brzezinski, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S. Sievi, M. Spiliopoulou, J. Stefanowski, SIGKDD Explorations 16 (2014) 1–10.

Extended Graph-Based Models for Enhanced Similarity Search in Cavbase
T. Krotzky, T. Fober, E. Hüllermeier, G. Klebe, IEEE/ACM Trans. Comput. Biology Bioinform. 11 (2014) 878–890.

Identification of Functionally Releated Enzymes by Learning to Rank Methods
M. Stock, T. Fober, E. Hüllermeier, S. Glinca, G. Klebe, T. Pahikkala, A. Airola, B. De Baets, W. Wageman, IEEE/ACM Trans. Comput. Biology Bioinform. 11 (2014) 1157–1169.

Semi-Automated Software Composition Through Generated Components
F. Mohr, H. Kleine Büning, in: Proceedings of the 15th International Conference on Information Integration and Web-Based Applications &; Services (IiWAS), 2013, pp. 676–680.

Reducing Nondeterminism in Automated Service Composition
F. Mohr, T. Lettmann, H. Kleine Büning, in: Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 2013, pp. 154–161.

Labelwise versus pairwise decomposition in label ranking
W. Cheng, S. Henzgen, E. Hüllermeier, in: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany, 2013, pp. 129–136.

Rectifying classifier chains for multi-label classification, Bamberg, Germany
R. Senge, J. del Coz, E. Hüllermeier, in: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany, 2013, pp. 151–158.

Preference-based evolutionary optimization using generalized racing algorithms
R. Busa-Fekete, T. Fober, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier (Eds.), In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2013, pp. 237–246.