Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics
V. Melnikov, E. Hüllermeier, in: Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017, 2017, pp. 1–12.

Learning TSK Fuzzy Rules from Data Streams
A. Shaker, W. Heldt, E. Hüllermeier, in: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia, 2017.

Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting
M. Bräuning, E. Hüllermeier, T. Keller, M. Glaum, European Journal of Operational Research 258 (2017) 295–306.

Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic
M.-C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, W. Schäfer, IEEE Transactions on Software Engineering 43 (2017) 739–759.

From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection
E. Hüllermeier, The Computing Research Repository  (CoRR) (2017).

Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem
A. Ramaswamy, S. Bhatnagar, Stochastics 88 (2016) 1173–1187.

Pairwise versus Pointwise Ranking: A Case Study
V. Melnikov, E. Hüllermeier, D. Kaimann, B. Frick,  Pritha  Gupta, Schedae Informaticae 25 (2016).

Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic
M.C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, W. Schäfer, IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017 (2016) 739–759.

Learning to Aggregate Using Uninorms
V. Melnikov, E. Hüllermeier, in: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.

Preference Learning
J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2016.

On the identifiability of models  in multi-criteria preference learning
C. Labreuche, E. Hüllermeier, P. Vojtas, A. Fallah Tehrani, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), In Proceedings DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, 2016.

Preference -based reinforcement learning using dyad ranking
D. Schäfer, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), In Proceedings DPL16 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany, 2016.

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, M. Ahmadi Fahandar, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), In Proceedings DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, 2016.

Support vector classification on noisy data using fuzzy superset losses
S. Lu, E. Hüllermeier, in: E. Hüllermeier, F. Hoffmann, R. Mikut (Eds.), In Proceedings 26th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2016, pp. 1–8.

Plackett-Luce networks for dyad ranking
D. Schäfer, E. Hüllermeier, in: In Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany, 2016.

Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts
K. Pfannschmidt, E. Hüllermeier, S. Held, R. Neiger, in: In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, Springer, 2016, pp. 450–461.

CavSimBase: A database for large scale comparison of protein binding sites
M. Leinweber, T. Fober, M. Strickert, L. Baumgärtner, G. Klebe, B. Freisleben, E. Hüllermeier, IEEE Transactions on Knowledge and Data Engineering 28 (2016) 1423–1434.

Towards Automated Service Composition Under Quality Constraints
F. Mohr, Towards Automated Service Composition Under Quality Constraints, Universität Paderborn, 2016.

Preference Learning
J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2016.

 Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany
F. Hoffmann, E. Hüllermeier, R. Mikut, eds.,  Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany, 2016.

Extreme F-measure maximization using sparse probability estimates 
K. Jasinska, K. Dembczynski, R. Busa-Fekete, T. Klerx, E. Hüllermeier, in: M.F. Balcan, K.Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, 2016.

Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016
V. Melnikov, E. Hüllermeier, in: European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 756–771.

Consistency of probalistic classifier trees
K. Dembczynski, W. Kotlowski, W. Waegeman, R. Busa-Fekete, E. Hüllermeier, in: In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 511–526.

Predicting the electricity consumption of buildings: An improved CBR approach
A. Shabani, A. Paul, R. Platon, E. Hüllermeier, in: In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, 2016, pp. 356–369.

Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts
K. Pfannschmidt, E. Hüllermeier, S. Held, R. Neiger, in: In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, Springer, 2016, pp. 450–461.

On the Identifiability of models in multi-criteria preference learning 
C. Labreuche, E. Hüllermeier, P. Vojtas, A. Fallah Tehrani, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, M. Ahmadi Fahandar, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Support vector classification on noisy data using fuzzy supersets losses
S. Lu, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, 2016, pp. 1–8.

Plackett-Luce networks for dyad ranking
D. Schäfer, E. Hüllermeier, in: In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.

ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence
G.A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, F. van Harmelen, eds., ECAI 2016, 22nd European Conference on Artificial Intelligence, Including PAIS 2016, Prestigious Applications of Artificial Intelligence, IOS Press, The Hague, The Netherlands, 2016.

CavSimBase: A database for large scale comparison of protein binding sites
M. Leinweber, T. Fober, M. Strickert, L. Baumgärtner, G. Klebe, B. Freisleben, E. Hüllermeier, IEEE Transactions on Knowledge and Data Engineering 28 (2016) 1423–1434.

Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification
M. Riemenschneider, R. Senge, U. Neumann, E. Hüllermeier, D. Heider, BioData Mining 9 (2016).

Market-Specific Service Compositions: Specification and Matching
S. Arifulina, M.C. Platenius, F. Mohr, G. Engels, W. Schäfer, in: Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 2015, pp. 333--340.

An approach towards adaptive service composition in markets of composed services
A. Jungmann, F. Mohr, Journal of Internet Services and Applications (2015) 1–18.

A Metric for Functional Reusability of Services
F. Mohr, in: Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313.

Automated Online Service Composition
F. Mohr, A. Jungmann, H. Kleine Büning, in: Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64.

Fast Fuzzy Pattern Tree Learning for Classification
R. Senge, E. Hüllermeier, IEEE Transactions on Fuzzy Systems 23 (2015) 2024–2033.

Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations
D. Schäfer, E. Hüllermeier, in: In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection Co-Located ECML/PKDD, Porto, Portugal, 2015, pp. 110–111.

A cbr approach to the angry birds game
A. Paul, E. Hüllermeier, in: In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 2015, pp. 68–77.

Depth estimation in monocular images: Quantitative versus qualitative approaches
R. Ewerth, A. Balz, J. Gehlhaar, K. Dembczynski, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier (Eds.), In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany, KIT Scientific Publishing, 2015, pp. 235–240.

Locally weighted regression through data imprecisiation
S. Lu, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier (Eds.), In Proceedings 25th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2015, pp. 97–104.

Fast fuzzy pattern tree learning for classification 
R. Senge, E. Hüllermeier, IEEE Transactions on Fuzzy Systems 23 (2015) 2024–2033.

From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions
E. Hüllermeier, Informatik Spektrum 38 (2015) 500–509.

Does machine learning need fuzzy logic?
E. Hüllermeier, Fuzzy Sets and Systems 281 (2015) 292–299.

On the Bayes-optimality of F-measure maximizers
W. Waegeman, K. Dembczynski, A. Jachnik, W. Cheng, E. Hüllermeier, Journal of Machine Learning Research 15 (2015) 3313–3368.

Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study
A. Shaker, E. Hüllermeier, Neurocomputing 150 (2015) 250–264.

Case-Based Reasoning Research and Development 
E. Hüllermeier, M. Minor, in: In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343, Springer, 2015.

Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing
F. Hoffmann, E. Hüllermeier, in: 2015.

Case Base Maintenance in Preference-Based CBR
A. Abdel-Aziz, E. Hüllermeier, in: In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 2015, pp. 1–14.