Jasinska, K., K. Dembczynski, Robert Busa-Fekete, Timo Klerx, and Eyke Hüllermeier. “Extreme F-Measure Maximization Using Sparse Probability Estimates .” In Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, edited by M.F. Balcan and K.Q. Weinberger, 2016.

Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms,  in Proceedings ECML/PKDD-2016.” In European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 756–71, 2016.

Dembczynski, K., W. Kotlowski, W. Waegeman, Robert Busa-Fekete, and Eyke Hüllermeier. “Consistency of Probalistic Classifier Trees.” In In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 511–26, 2016.

Shabani, Aulon, Adil Paul, R. Platon, and Eyke Hüllermeier. “Predicting the Electricity Consumption of Buildings: An Improved CBR Approach.” In In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, 356–69, 2016.

Pfannschmidt, Karlson, Eyke Hüllermeier, S. Held, and R. Neiger. “Evaluating Tests in Medical  Diagnosis-Combining Machine Learning with Game-Theoretical Concepts.” In In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, 450–61. Springer, 2016.

Labreuche, C., Eyke Hüllermeier, P. Vojtas, and A. Fallah Tehrani. “On the Identifiability of Models in Multi-Criteria Preference Learning .” In Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete, Eyke Hüllermeier, V. Mousseau, and Karlson Pfannschmidt, 2016.

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” In Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete, Eyke Hüllermeier, V. Mousseau, and Karlson Pfannschmidt, 2016.

Couso, Ines, Mohsen Ahmadi Fahandar, and Eyke Hüllermeier. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” In Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete, Eyke Hüllermeier, V. Mousseau, and Karlson Pfannschmidt, 2016.

Lu, S., and Eyke Hüllermeier. “Support Vector Classification on Noisy Data Using Fuzzy Supersets Losses.” In Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, edited by F. Hoffmann, Eyke Hüllermeier, and R. Mikut, 1–8, 2016.

Schäfer, Dirk, and Eyke Hüllermeier. “Plackett-Luce Networks for Dyad Ranking.” In In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.

Kaminka, G.A., M. Fox, P. Bouquet, Eyke Hüllermeier, V. Dignum, F. Dignum, and F. van Harmelen, eds. ECAI 2016, 22nd European Conference on Artificial Intelligence, Including PAIS 2016, Prestigious Applications of Artificial Intelligence. Vol. 285. Frontiers in Artificial Intelligence and Applications, The Hague, The Netherlands. The Hague, The Netherlands: IOS Press, 2016.

Leinweber, M., T. Fober, M. Strickert, L. Baumgärtner, G. Klebe, B. Freisleben, and Eyke Hüllermeier. “CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites.” IEEE Transactions on Knowledge and Data Engineering 28, no. 6 (2016): 1423–34.

Riemenschneider, M., Robin Senge, U. Neumann, Eyke Hüllermeier, and D. Heider. “Exploiting HIV-1 Protease and Reverse Transcriptase Cross-Resistance Information for Improved Drug Resistance Prediction by Means of Multi-Label Classification.” BioData Mining 9, no. 10 (2016).

Arifulina, Svetlana, Marie Christin Platenius, Felix Mohr, Gregor Engels, and Wilhelm Schäfer. “Market-Specific Service Compositions: Specification and Matching.”https://doi.org/10.1109/SERVICES.2015.58__In Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 333--340, 2015.https://doi.org/10.1109/SERVICES.2015.58 .

Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.”https://doi.org/10.1186/s13174-015-0022-8__Journal of Internet Services and Applications, no. 1 (2015): 1–18.https://doi.org/10.1186/s13174-015-0022-8 .

Mohr, Felix. “A Metric for Functional Reusability of Services.”https://doi.org/10.1007/978-3-319-14130-5_21__In Proceedings of the 14th International Conference on Software Reuse (ICSR), 298--313. LNCS, 2015.https://doi.org/10.1007/978-3-319-14130-5_21 .

Mohr, Felix, Alexander Jungmann, and Hans Kleine Büning. “Automated Online Service Composition.”https://doi.org/10.1109/SCC.2015.18__In Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 57--64, 2015.https://doi.org/10.1109/SCC.2015.18 .

Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning for Classification.”https://doi.org/10.1109/tfuzz.2015.2396078__IEEE Transactions on Fuzzy Systems 23, no. 6 (2015): 2024–33.https://doi.org/10.1109/tfuzz.2015.2396078 .

Schäfer, D., and Eyke Hüllermeier. “Preference-Based Meta-Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations.” In In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection Co-Located ECML/PKDD, Porto, Portugal, 110–11, 2015.

Paul, Adil, and Eyke Hüllermeier. “A Cbr Approach to the Angry Birds Game.” In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 68–77, 2015.

Ewerth, R., A. Balz, J. Gehlhaar, K. Dembczynski, and Eyke Hüllermeier. “Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches.” In In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany, edited by F. Hoffmann and Eyke Hüllermeier, 235–40. KIT Scientific Publishing, 2015.

Lu, S., and Eyke Hüllermeier. “Locally Weighted Regression through Data Imprecisiation.” In In Proceedings 25th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann and Eyke Hüllermeier, 97–104. KIT Scientific Publishing, 2015.

Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning for Classification .” IEEE Transactions on Fuzzy Systems 23, no. 6 (2015): 2024–33.

Hüllermeier, Eyke. “From Knowledge-Based to Data Driven Fuzzy Modeling: Development, Criticism and Alternative Directions.” Informatik Spektrum 38, no. 6 (2015): 500–509.

Hüllermeier, Eyke. “Does Machine Learning Need Fuzzy Logic?” Fuzzy Sets and Systems 281 (2015): 292–99.

Waegeman, W., K. Dembczynski, A. Jachnik, W. Cheng, and Eyke Hüllermeier. “On the Bayes-Optimality of F-Measure Maximizers.” Journal of Machine Learning Research 15 (2015): 3313–68.

Shaker, A., and Eyke Hüllermeier. “Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams: Experimental Design and Case Study.” Neurocomputing 150 (2015): 250–64.

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

Hoffmann, F., and Eyke Hüllermeier. “Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing,” 2015.

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

Szörényi, B., Robert Busa-Fekete, P. Weng, and Eyke Hüllermeier. “Qualitative Multi-Armed Bandits: A Quantile-Based Approach.” In In Proceedings International Conference on Machine Learning (ICML 2015), 1660–68, 2015.

Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using A Bilinear Plackett-Luce Model.” In In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 227–42, 2015.

Hüllermeier, Eyke, and W. Cheng. “Superset Learning Based on Generalized Loss Minimization .” In In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 260–75, 2015.

Henzgen, Sascha, and Eyke Hüllermeier. “Weighted Rank Correlationf: A Flexible Approach Based on Fuzzy Order Relations.” In In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 422–37, 2015.

Szörényi, B., Robert Busa-Fekete, Adil Paul, and Eyke Hüllermeier. “Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach.” In In Advances in Neural Information Processing Systems 28 (NIPS 2015), 604–12, 2015.

Szörényi, B., Robert Busa-Fekete, K. Dembczynski, and Eyke Hüllermeier. “Online F-Measure Optimization.” In In Advances in Neural Information Processing Systems 28 (NIPS 2015), 595–603, 2015.

El Mesaoudi-Paul, Adil, and Eyke Hüllermeier. “A CBR Approach to the Angry Birds Game.” In In Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 68–77, 2015.

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations.” In In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML), 110–11, 2015.

Lu, S., and Eyke Hüllermeier. “Locally Weighted Regression through Data Imprecisiation.” In Proceedings 25. Workshop Computational Intelligence, 97–104, 2015.

Ewerth, Ralph, A. Balz, J. Gehlhaar, K. Dembczynski, and Eyke Hüllermeier. “Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches.” In Proceedings 25. Workshop Computational Intelligence, 235–40, 2015.

Waegeman, W., K. Dembczynski, A. Jachnik, W. Cheng, and Eyke Hüllermeier. “On the Bayes-Optimality of F-Measure Maximizers.” In Journal of Machine Learning Research 15 (2015): 3333–88.

Hüllermeier, Eyke. “Does Machine Learning Need Fuzzy Logic?” Fuzzy Sets and Systems 281 (2015): 292–99.

Shaker, Ammar, and Eyke Hüllermeier. “Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams: Experimental Design and Case Study.” Neurocomputing 150 (2015): 250–64.

Hüllermeier, Eyke. “From Knowledge-Based to Data-Driven Fuzzy Modeling-Development, Criticism and Alternative Directions.” Informatik Spektrum 38, no. 6 (2015): 500–509.

Garcia-Jimenez, S., U. Bustince, Eyke Hüllermeier, R. Mesiar, N.R. Pal, and A. Pradera. “Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems.” IEEE Transactions on Fuzzy Systems 23, no. 4 (2015): 1259–73.

Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning of Classification.” IEEE Transactions on Fuzzy Systems 23, no. 6 (2015): 2024–33.

Basavaraju, Manu, L Sunil Chandran, Deepak Rajendraprasad, and Arunselvan Ramaswamy. “Rainbow Connection Number of Graph Power and Graph Products.” Graphs and Combinatorics 30, no. 6 (2014): 1363–82.

Basavaraju, Manu, L Sunil Chandran, Deepak Rajendraprasad, and Arunselvan Ramaswamy. “Rainbow Connection Number and Radius.” Graphs and Combinatorics 30, no. 2 (2014): 275–85.

Mohr, Felix, and Sven Walther. “Template-Based Generation of Semantic Services.”https://doi.org/10.1007/978-3-319-14130-5_14__In Proceedings of the 14th International Conference on Software Reuse (ICSR), 188–203. LNCS, 2014.https://doi.org/10.1007/978-3-319-14130-5_14 .

Jungmann, Alexander, Felix Mohr, and Bernd Kleinjohann. “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services.”https://doi.org/10.1109/SERVICES.2014.68__In Proceedings of the 10th World Congress on Services (SERVICES), 346–53, 2014.https://doi.org/10.1109/SERVICES.2014.68 .