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.

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.

Efficient learning of classifiers based on the 2-additive Choquet integral
E. Hüllermeier, A. Fallah Tehrani, in: C. Moewes, A. Nürnberger (Eds.), Computational Intelligence in Intelligent Data Analysis, Springer, 2012, pp. 17–30.

Learning conditional lexicographic preference trees
M. Bräuning, E. Hüllermeier, in: In Workshops on Preference Learning at ECAI, European Conference on Artiticial Intelligence, Montpellier, France, 2012.

On the VC dimension of the Choquet integral
E. Hüllermeier, A. Fallah Tehrani, in: In Proceedings IPMU-2012 14th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Catania, Italy, 2012.

Learning  monotone nonlinear models using the Choquet integral
A. Fallah Tehrani, W. Cheng, K. Dembczynski, E. Hüllermeier, Machine Learning 89 (2012) 183–211.

Grouping, overlap and generalized bientropic functions for fuzzy modeling of pairwise comparisons
H. Bustince, M. Pagola, R. Mesiar, E. Hüllermeier, F. Herrera, IEEE Transactions on Fuzzy Systems 20 (2012) 405–415.

Preference-based reinforcement learning: A formal framework and a policy iteration algorithm
J. Fürnkranz, E. Hüllermeier, W. Cheng, S.H. Park, Machine Learning 89 (2012) 123–156.

On label dependence and loss  minimization in multi-label classification
K. Dembczynski, W. Waegeman, W. Cheng, E. Hüllermeier, Machine Learning 88 (2012) 5–45.

IBL Streams: A system for instance-based classification and regression on data streams
A. Shaker, E. Hüllermeier, Evolving Systems 3 (2012) 235–249.

A formal and empirical analysis of the fuzzy gamma rank correlation coefficient
M. Dolorez Ruiz, E. Hüllermeier, Information Sciences 206 (2012) 1–17.

Preference learning using the Choquet integral: The case of multipartite ranking
A. Fallah Tehrani, W. Cheng, E. Hüllermeier, IEEE Transactions on Fuzzy Systems 20 (2012) 1102–1113.

Fingerprint kernels for protein structure comparison
T. Fober, M. Mernberger, G. Klebe, E. Hüllermeier, Molecular Informatics 31 (2012) 443–452.

Comparing fuzzy partitions: A generalization of the Rand index and related measures
E. Hüllermeier, M. Rifqi, S. Henzgen, R. Senge, IEEE Transactions on Fuzzy Systems 20 (2012) 546–556.

Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung
R. Senge, T. Fober, N. Nasiri, E. Hüllermeier, At-Atomatisierungstechnik 60 (2012) 622–629.

Label ranking with partial abstention based on thresholded probalistic models
W. Cheng, E. Hüllermeier, W. Waegeman, V. Welker, in: In Proceedings NIPS-2012, 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012.

Probability estimation for mulit-class classification based on label ranking
W. Cheng, E. Hüllermeier, in: Proceedings ECML/PKDD-2012, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, UK, 2012.

Consistent multilabel ranking through univariate loss minimization
K. Dembczynski, W. Kotlowski, E. Hüllermeier, in: J. Langford, J. Pineau (Eds.), In Proceedings ICML-2012,  International Conference on Machine Learning, Edinburgh, Scotland, 2012.

An analysis of chaining in multi-label classification
K. Dembczynski, W. Waegeman, E. Hüllermeier, in: In Proceedings ECAI-2012, 20th European Conference on Artificial Inteligence, Montpellier, France , IOS Press, 2012, pp. 294–299.

Fuzzy rules in data mining: From fuzzy associations to gradual dependencies
E. Hüllermeier, in: E. Trillas, P.P. Bonissone, L. Magdalena, J. Kacprzyk (Eds.), Combining Experimentation and Theory, Springer, 2012, pp. 123–135.

Joint mode estimation in multi-label classification by chaining
K. Dembczynski, W. Waegeman, E. Hüllermeier, in: In Proceedings ECML Workshop on Collective Inference and Learning on Structured Data, Athens, Greece, 2011.

Prediction of protein localization for specialized compartments using time series kernels
M. Mernberger, D. Moog, S. Stork, S. Zauner, U. Maier, E. Hüllermeier, in: In Proceedings GCB-2011 German Conference on Bioinformatics Munich, Germany, 2011.