Kaminka, G. A., et al., editors. ECAI 2016, 22nd European Conference on Artificial Intelligence, Including PAIS 2016, Prestigious Applications of Artificial Intelligence. Vol. 285, IOS Press, 2016.

Leinweber, M., et al. “CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites.” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, 2016, pp. 1423–34.

Riemenschneider, M., et al. “Exploiting HIV-1 Protease and Reverse Transcriptase Cross-Resistance Information for Improved Drug Resistance Prediction by Means of Multi-Label Classification.” BioData Mining, vol. 9, no. 10, 2016.

Arifulina, Svetlana, et al. “Market-Specific Service Compositions: Specification and Matching.”10.1109/SERVICES.2015.58__Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 2015, pp. 333--340, doi:10.1109/SERVICES.2015.58.

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

Mohr, Felix. “A Metric for Functional Reusability of Services.”10.1007/978-3-319-14130-5_21__Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313, doi:10.1007/978-3-319-14130-5_21.

Mohr, Felix, et al. “Automated Online Service Composition.”10.1109/SCC.2015.18__Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64, doi:10.1109/SCC.2015.18.

Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning for Classification.”10.1109/tfuzz.2015.2396078__IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, Institute of Electrical and Electronics Engineers (IEEE), 2015, pp. 2024–33, doi:10.1109/tfuzz.2015.2396078.

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

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

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

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

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

Hüllermeier, Eyke. “From Knowledge-Based to Data Driven Fuzzy Modeling: Development, Criticism and Alternative Directions.” Informatik Spektrum, vol. 38, no. 6, 2015, pp. 500–09.

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

Waegeman, W., et al. “On the Bayes-Optimality of F-Measure Maximizers.” Journal of Machine Learning Research, vol. 15, 2015, pp. 3313–68.

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

Hüllermeier, Eyke, and M. Minor. “Case-Based Reasoning Research and Development .” 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 Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 2015, pp. 1–14.

Szörényi, B., et al. “Qualitative Multi-Armed Bandits: A Quantile-Based Approach.” In Proceedings International Conference on Machine Learning (ICML 2015), 2015, pp. 1660–68.

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

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

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

Szörényi, B., et al. “Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach.” In Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 604–12.

Szörényi, B., et al. “Online F-Measure Optimization.” In Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 595–603.

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

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

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

Ewerth, Ralph, et al. “Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches.” Proceedings 25. Workshop Computational Intelligence, 2015, pp. 235–40.

Waegeman, W., et al. “On the Bayes-Optimality of F-Measure Maximizers.” In Journal of Machine Learning Research, vol. 15, 2015, pp. 3333–88.

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

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

Hüllermeier, Eyke. “From Knowledge-Based to Data-Driven Fuzzy Modeling-Development, Criticism and Alternative Directions.” Informatik Spektrum, vol. 38, no. 6, 2015, pp. 500–09.

Garcia-Jimenez, S., et al. “Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems.” IEEE Transactions on Fuzzy Systems, vol. 23, no. 4, 2015, pp. 1259–73.

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

Basavaraju, Manu, et al. “Rainbow Connection Number of Graph Power and Graph Products.” Graphs and Combinatorics, vol. 30, no. 6, Springer, 2014, pp. 1363–82.

Basavaraju, Manu, et al. “Rainbow Connection Number and Radius.” Graphs and Combinatorics, vol. 30, no. 2, Springer, 2014, pp. 275–85.

Mohr, Felix, and Sven Walther. “Template-Based Generation of Semantic Services.”10.1007/978-3-319-14130-5_14__Proceedings of the 14th International Conference on Software Reuse (ICSR), 2014, pp. 188–203, doi:10.1007/978-3-319-14130-5_14.

Jungmann, Alexander, et al. “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services.”10.1109/SERVICES.2014.68__Proceedings of the 10th World Congress on Services (SERVICES), 2014, pp. 346–53, doi:10.1109/SERVICES.2014.68.

Jungmann, Alexander, et al. “Applying Reinforcement Learning for Resolving Ambiguity in Service Composition.”10.1109/SOCA.2014.48__Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 2014, pp. 105–12, doi:10.1109/SOCA.2014.48.

Mohr, Felix. “Estimating Functional Reusability of Services.” Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 2014, pp. 411–18.

Agarwal, M., et al. “Preference-Based Learning of Ideal Solutions in TOPSIS-like Decision Models.” Journal of Multi-Criteria Decision Analysis, vol. 22, no. 3–4, 2014.

Krotzky, T., et al. “Extended Graph-Based Models for Enhanced Similarity Search in Cabase.” IEEE/ACM Transactions of Computational Biology and Bioinformatics, vol. 11, no. 5, 2014, pp. 878–90.

Hüllermeier, Eyke. “Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization.” International Journal of Approximate Reasoning, vol. 55, no. 7, 2014, pp. 1519–34.

Henzgen, Sascha, et al. “Visualization of Evolving Fuzzy-Rule-Based Systems.” Evolving Systems, vol. 5, 2014, pp. 175–91.

Busa-Fekete, Robert, et al. “Preference-Based Reinforcement Learning: Evolutionary Direct Policy Search Using a Preference-Based Racing Algorithm.” Machine Learning, vol. 97, no. 3, 2014, pp. 327–51.

Krempl, G., et al. “Open Challenges for Data Stream Mining Research.” SIGKDD Explorations, vol. 16, no. 1, 2014, pp. 1–10.

Strickert, M., et al. “Correlation-Based Embedding of Pairwise Score Data.” Neurocomputing, vol. 141, 2014, pp. 97–109.

Shaker, Ammar, and Eyke Hüllermeier. “Survival Analysis on Data Streams: Analyzing Temporal Events in Dynamically Changing Environments.” International Journal of Applied Mathematics and Computer Science, vol. 24, no. 1, 2014, pp. 199–212.