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.

The comprehensive diagnostic study is suggested as a design to model the diagnostic process
N. Donner-Banzhoff, J. Haasenritter, E. Hüllermeier, A. Viniol, S. Bösner, A. Becker, Journal of Clinical Epidemiology 2 (2014) 124–132.

PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences
R. Busa-Fekete, B. Szörényi, E. Hüllermeier, in: Proceedings AAAI 2014, Quebec, Canada, 2014, pp. 1701–1707.

A Survey of Preference-Based Online Learning with Bandit Algorithms
R. Busa-Fekete, E. Hüllermeier, in: Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia, 2014, pp. 18–39.

Mining Rank Data
S. Henzgen, E. Hüllermeier, in: Proceedings Discovery Science, Bled,Slovenia , 2014, pp. 123–134.

The Choquet kernel for monotone data
A. Fallah Tehrani, M. Strickert, E. Hüllermeier, in: Proceedings ESANN , Bruges, Belgium, 2014.

Learning Solution Similarity in Preference-Based CBR
A. Abdel-Aziz, M. Strickert, E. Hüllermeier, in: Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland, 2014, pp. 17–31.

Dyad Ranking Using A Bilinear Plackett-Luce Model
D. Schäfer, E. Hüllermeier, in: Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany, 2014, pp. 32–33.

Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France
T. Calders, F. Esposito, E. Hüllermeier, R. Meo, in: Proceedings, Parts I-III. Lecture Notes in Computer Science, Springer, 2014, pp. 8724–8726.

Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports
J. Fürnkranz, E. Hüllermeier, C. Rudin, R. Slowinski, S. Sanner, in: 2014, pp. 1–27.

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.