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.

Qualitative Multi-Armed Bandits: A Quantile-Based Approach
B. Szörényi, R. Busa-Fekete, P. Weng, E. Hüllermeier, in: In Proceedings International Conference on Machine Learning (ICML 2015), 2015, pp. 1660–1668.

Dyad Ranking Using A Bilinear Plackett-Luce Model
D. Schäfer, E. Hüllermeier, in: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2015, pp. 227–242.

Superset Learning Based on Generalized Loss Minimization 
E. Hüllermeier, W. Cheng, in: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2015, pp. 260–275.

Weighted Rank Correlation : A Flexible Approach Based on Fuzzy Order Relations
S. Henzgen, E. Hüllermeier, in: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2015, pp. 422–437.

Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach
B. Szörényi, R. Busa-Fekete, A. Paul, E. Hüllermeier, in: In Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 604–612.

Online F-Measure Optimization
B. Szörényi, R. Busa-Fekete, K. Dembczynski, E. Hüllermeier, in: In Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 595–603.

A CBR Approach to the Angry Birds Game
A. El Mesaoudi-Paul, E. Hüllermeier, in: In Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 2015, pp. 68–77.

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 (MetaSel@PKDD/ECML), 2015, pp. 110–111.

Locally weighted regression through data imprecisiation
S. Lu, E. Hüllermeier, in: Proceedings 25. Workshop Computational Intelligence, 2015, pp. 97–104.

Depth estimation in monocular images: Quantitative versus qualitative approaches
R. Ewerth, A. Balz, J. Gehlhaar, K. Dembczynski, E. Hüllermeier, in: Proceedings 25. Workshop Computational Intelligence, 2015, pp. 235–240.

On the Bayes-Optimality of F-Measure Maximizers
W. Waegeman, K. Dembczynski, A. Jachnik, W. Cheng, E. Hüllermeier, In Journal of Machine Learning Research 15 (2015) 3333–3388.

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

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

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

Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems
S. Garcia-Jimenez, U. Bustince, E. Hüllermeier, R. Mesiar, N.R. Pal, A. Pradera, IEEE Transactions on Fuzzy Systems 23 (2015) 1259–1273.

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

Rainbow connection number of graph power and graph products
M. Basavaraju, L.S. Chandran, D. Rajendraprasad, A. Ramaswamy, Graphs and Combinatorics 30 (2014) 1363–1382.

Rainbow connection number and radius
M. Basavaraju, L.S. Chandran, D. Rajendraprasad, A. Ramaswamy, Graphs and Combinatorics 30 (2014) 275–285.

Template-based Generation of Semantic Services
F. Mohr, S. Walther, in: Proceedings of the 14th International Conference on Software Reuse (ICSR), 2014, pp. 188–203.

Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services
A. Jungmann, F. Mohr, B. Kleinjohann, in: Proceedings of the 10th World Congress on Services (SERVICES), 2014, pp. 346–353.

Applying Reinforcement Learning for Resolving Ambiguity in Service Composition
A. Jungmann, F. Mohr, B. Kleinjohann, in: Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 2014, pp. 105–112.

Estimating Functional Reusability of Services
F. Mohr, in: Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 2014, pp. 411–418.

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.

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.