Weighted rank correlation measures based on fuzzy order relations
S. Henzgen, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier (Eds.), In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2013, pp. 227–236.

Interactive Q-learning with ordinal rewards and unreliable tutor
P. Weng, R. Busa-Fekete, E. Hüllermeier, in: In Proceedings ECML/PKDD-Workshop on Reinforcement Learning from Generalized Feedback:Beyond Numerical Rewards, Prague, 2013.

Preference-based evolutionary direct policy search
R. Busa-Fekete, B. Szörenyi, P. Weng, E. Hüllermeier, in: In Proceedings ECML/PKDD-Workshop on Reinforcement Learning from Generalized Feedback:Beyond Numerical Rewards, Prague, 2013.

A nearest neigbor approach to label ranking based on generalized labelwise loss minimization
W. Cheng, E. Hüllermeier, in: In Proceedings M-PREF, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China, 2013.

Event history analysis on data streams: An application to earthquake occurence
A. Shaker, E. Hüllermeier, in: G. Krempl, I. Zliobaite, Y. Wang, G. Forman (Eds.), In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic, 2013, pp. 38–41.

On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery
R. Senge, J.J. del Coz, E. Hüllermeier, in: L. Schmidt-Thieme, M. Spiliopoulou (Eds.), In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany , Springer, 2013.

Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics, Algorithms from and for Nature and Life
T. Fober, G. Klebe, E. Hüllermeier, in: B. Lausen, D. Van den Poel, A. Ultsch (Eds.), In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany, Springer, 2013, pp. 279–286.

Ordinal Choquistic regression 
A. Fallah Tehrani, E. Hüllermeier, in: J. Montero, G. Pasi, D. Ciucci (Eds.), In Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy, Atlantis Press, 2013.

Fuzzy Pattern Trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of colour yield in poyester dyeing, Edmonton, Canada
N. Nasiri, T. Fober, R. Senge, E. Hüllermeier, in: In Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada, 2013, pp. 715–721.

Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction
D. Heider, R. Senge, W. Cheng, E. Hüllermeier, Bioinformatics 29 (2013) 1946–1952.

Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. 
S. Bösner, K. Bönisch, J. Haasenritter , P. Schlegel, E. Hüllermeier, N. Donner-Banzhoff, BMC Family Practice 14 (2013) 154–162.

Diagnose im Kontext - eine erweiterte Perspektive
J. Haasenritter, A. Viniol, A. Becker, S. Bösner, E. Hüllermeier, R. Senge, N. Donner-Banzhoff, Zeitschrift Für Evidenz, Fortbildung Und Qualität Im Gesundheitswesen (ZEFQ) 107 (2013) 585–591.

Evolving fuzzy pattern trees for binary classification on data streams
A. Shaker, R. Senge, E. Hüllermeier, Information Sciences 220 (2013) 34–45.

Learning to rank lexical substitutions
G. Szarvas, R. Busa-Fekete, E. Hüllermeier, in: In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA, 2013.

Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization
K. Dembczynski, A. Jachnik, W. Kotlowski, W. Waegeman, E. Hüllermeier, in: S. Dasgupta, D. McAllester (Eds.), In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, 2013, pp. 1130–1138.

Top-k selection based on adaptive sampling of noisy preferences
R. Busa-Fekete, B. Szoreny, P. Weng, W. Cheng, E. Hüllermeier, in: S. Dasgupta, D. McAllester (Eds.), In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, 2013, pp. 1094–1102.

Preference-based CBR: General ideas and basic principles
E. Hüllermeier, W. Cheng, in: F. Rossi (Ed.), In Proceedings IJCAI-13, 23rd International Joint Conference on Artificial Intelligence, Beijing, China, AAAI Press, 2013, pp. 3012–3016.

Rule chains for visualizing evolving fuzzy rule-based systems
S. Henzgen, M. Strickert, E. Hüllermeier, in: R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, A. Zolnierek (Eds.), In Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, Springer, 2013, pp. 279–288.

Recovery analysis for adaptive learning from non-stationary data streams
A. Shaker, E. Hüllermeier, in: R. Burduk, K. Jackowski, M. Kurzynski, W. Wozniak, A. Zolnierek (Eds.), In Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, Springer, 2013, pp. 289–298.

GPU-based cloud computing for comparing the structure of protein binding sites
M. Leinweber, L. Baumgärtner, M. Mernberger, T. Fober, E. Hüllermeier, G. Klebe, B. Freisleben, in: In Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione D`Italia, Italy, 2012.