Preference-based learning of ideal solutions in TOPSIS-like decision models
M. Agarwal, A. Fallah Tehrani, E. Hüllermeier, Journal of Multi-Criteria Decision Analysis 22 (2014).

Extended graph-based models for enhanced similarity search in Cabase
T. Krotzky, T. Fober, E. Hüllermeier, G. Klebe, IEEE/ACM Transactions of Computational Biology and Bioinformatics 11 (2014) 878–890.

Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
E. Hüllermeier, International Journal of Approximate Reasoning 55 (2014) 1519–1534.

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

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

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.

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

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

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.

Fast Collisionless Pattern Formation by Anonymous, Position-Aware Robots
T. Lukovszki, F. Meyer auf der Heide, in: Lecture Notes in Computer Science, Cham, 2014.

Continuous Aggregation in Dynamic Ad-Hoc Networks
S. Abshoff, F. Meyer auf der Heide, in: Structural Information and Communication Complexity, Cham, 2014.

Common Fate Graph Patterns in Monte Carlo Tree Search for Computer Go
T. Graf, M. Platzner, in: 2014 IEEE Conference on Computational Intelligence and Games, 2014, pp. 1–8.

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.