Shaker, Ammar, and Eyke Hüllermeier. “Survival Analysis on Data Streams: Analyzing Temporal Events in Dynamically Changing Environments.” Applied Mathematics and Computer Science, vol. 24, no. 1, 2014, pp. 199–212.
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Hoffmann, F., et al. “Ausgewählte Beiträge Des GMA-Fachausschusses 5.14.” Computational Intelligence Automatisierungstechnik, vol. 62, no. 10, 2014, pp. 685–86.
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Calders, T., et al. “Guest Editors`introduction:Special Issue of the ECML/PKDD 2014 Journal Track.” Data Min. Knowledge Discovery, vol. 28, no. 5–6, 2014, pp. 1129–33.
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Henzgen, Sascha, et al. “Visualization of Evolving Fuzzy Rule-Based Systems.” Evolving Systems, vol. 5, no. 3, 2014, pp. 175–91.
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Hüllermeier, Eyke. “Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization.” Int. J. Approx. Reasoning, vol. 55, no. 7, 2014, pp. 1519–34.
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Hüllermeier, Eyke. “Rejoinder on "Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization.” Int. J. Approx. Reasoning, vol. 55, no. 7, 2014, pp. 1609–13.
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Strickert, M., et al. “Correlation-Based Embedding of Pairwise Score Data.” Neurocomputing, vol. 141, 2014, pp. 97–109.
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Senge, Robin, et al. “Reliable Classification: Learning Classifiers That Distinguish Aleatoric and Epistemic Uncertainty.” Information Sciences, vol. 255, 2014, pp. 16–29.
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Mernberger, M., et al. “Protein Sub-Cellular Localization Prediction for Special Compartments via Optimized Time Series Distances.” J. Bioinformatics and Computational Biology, vol. 12, no. 1, 2014.
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Calders, T., et al. “Guest Editors`introduction:Special Issue of the ECML/PKDD 2014 Journal Track.” Machine Learning, vol. 97, no. 1–2, 2014, pp. 1–3.
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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.
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Montanés, E., et al. “Dependent Binary Relevance Models for Multi-Label Classification.” Pattern Recognition, vol. 47, no. 3, 2014, pp. 1494–508.
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Krempl, G., et al. “Open Challenges for Data Stream Mining Research.” SIGKDD Explorations, vol. 16, no. 1, 2014, pp. 1–10.
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Krotzky, T., et al. “Extended Graph-Based Models for Enhanced Similarity Search in Cavbase.” IEEE/ACM Trans. Comput. Biology Bioinform., vol. 11, no. 5, 2014, pp. 878–90.
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Stock, M., et al. “Identification of Functionally Releated Enzymes by Learning to Rank Methods.” IEEE/ACM Trans. Comput. Biology Bioinform., vol. 11, no. 6, 2014, pp. 1157–69.
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Mohr, Felix, and Hans Kleine Büning. “Semi-Automated Software Composition Through Generated Components.”10.1145/2539150.2539235__Proceedings of the 15th International Conference on Information Integration and Web-Based Applications &; Services (IiWAS), 2013, pp. 676–80, doi:10.1145/2539150.2539235.
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DOI: 10.1145/2539150.2539235

Mohr, Felix, et al. “Reducing Nondeterminism in Automated Service Composition.”10.1109/SOCA.2013.25__Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 2013, pp. 154–61, doi:10.1109/SOCA.2013.25.
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Cheng, W., et al. “Labelwise versus Pairwise Decomposition in Label Ranking.” In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany, 2013, pp. 129–36.
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Senge, Robin, et al. “Rectifying Classifier Chains for Multi-Label Classification, Bamberg, Germany.” In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany, 2013, pp. 151–58.
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Busa-Fekete, Robert, et al. “Preference-Based Evolutionary Optimization Using Generalized Racing Algorithms.” In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann and Eyke Hüllermeier, KIT Scientific Publishing, 2013, pp. 237–46.
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Henzgen, S., and Eyke Hüllermeier. “Weighted Rank Correlation Measures Based on Fuzzy Order Relations.” In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann and Eyke Hüllermeier, KIT Scientific Publishing, 2013, pp. 227–36.
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Weng, P., et al. “Interactive Q-Learning with Ordinal Rewards and Unreliable Tutor.” In Proceedings ECML/PKDD-Workshop on Reinforcement Learning from Generalized Feedback:Beyond Numerical Rewards, Prague, 2013.
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Busa-Fekete, Robert, et al. “Preference-Based Evolutionary Direct Policy Search.” In Proceedings ECML/PKDD-Workshop on Reinforcement Learning from Generalized Feedback:Beyond Numerical Rewards, Prague, 2013.
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Cheng, W., and Eyke Hüllermeier. “A Nearest Neigbor Approach to Label Ranking Based on Generalized Labelwise Loss Minimization.” In Proceedings M-PREF, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China, 2013.
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Shaker, Ammar, and Eyke Hüllermeier. “Event History Analysis on Data Streams: An Application to Earthquake Occurence.” In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic, edited by G. Krempl et al., 2013, pp. 38–41.
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Senge, Robin, et al. “On the Problem of Error Propagation in Classier Chains for Multi-Label Classification. Data Analysis, Machine Learning and Knowledge Discovery.” In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany , edited by L. Schmidt-Thieme and M. Spiliopoulou, Springer, 2013.
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Fober, T., et al. “Local Clique Merging: An Extension of the Maximum Common Subgraph Measure with Applications in Structural Bioinformatics, Algorithms from and for Nature and Life.” In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany, edited by B. Lausen et al., Springer, 2013, pp. 279–86.
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Fallah Tehrani, A., and Eyke Hüllermeier. “Ordinal Choquistic Regression .” In Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy, edited by J. Montero et al., Atlantis Press, 2013.
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Nasiri, N., et al. “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.” In Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada, 2013, pp. 715–21.
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Heider, D., et al. “Multilabel Classification for Exploiting Cross-Resistance Information in HIV-1 Drug Resistence Prediction.” Bioinformatics, vol. 29, no. 16, 2013, pp. 1946–52.
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Bösner, S., et al. “Chest Pain in Primary Care: Is the Localization of Pain Diagnostically Helpful in the Critical Evaluation of Patients? A Cross Sectional Study. .” BMC Family Practice, vol. 14, no. 1, 2013, pp. 154–62.
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Haasenritter, J., et al. “Diagnose Im Kontext - Eine Erweiterte Perspektive.” Zeitschrift Für Evidenz, Fortbildung Und Qualität Im Gesundheitswesen (ZEFQ), vol. 107, 2013, pp. 585–91.
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Shaker, A., et al. “Evolving Fuzzy Pattern Trees for Binary Classification on Data Streams.” Information Sciences, vol. 220, 2013, pp. 34–45.
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Szarvas, G., et al. “Learning to Rank Lexical Substitutions.” In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA, 2013.
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Dembczynski, K., et al. “Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization.” In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, edited by S. Dasgupta and D. McAllester, 2013, pp. 1130–38.
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Busa-Fekete, Robert, et al. “Top-k Selection Based on Adaptive Sampling of Noisy Preferences.” In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, edited by S. Dasgupta and D. McAllester, 2013, pp. 1094–102.
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Hüllermeier, Eyke, and W. Cheng. “Preference-Based CBR: General Ideas and Basic Principles.” In Proceedings IJCAI-13, 23rd International Joint Conference on Artificial Intelligence, Beijing, China, edited by F. Rossi, AAAI Press, 2013, pp. 3012–16.
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Henzgen, Sascha, et al. “Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems.” In Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, edited by R. Burduk et al., Springer, 2013, pp. 279–88.
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Shaker, Ammar, and Eyke Hüllermeier. “Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams.” In Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, edited by R. Burduk et al., Springer, 2013, pp. 289–98.
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Leinweber, M., et al. “GPU-Based Cloud Computing for Comparing the Structure of Protein Binding Sites.” In Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione D`Italia, Italy, 2012.
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Hüllermeier, Eyke, and A. Fallah Tehrani. “Efficient Learning of Classifiers Based on the 2-Additive Choquet Integral.” Computational Intelligence in Intelligent Data Analysis, edited by C. Moewes and A. Nürnberger, Springer, 2012, pp. 17–30.
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Bräuning, M., and Eyke Hüllermeier. “Learning Conditional Lexicographic Preference Trees.” In Workshops on Preference Learning at ECAI, European Conference on Artiticial Intelligence, Montpellier, France, 2012.
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Hüllermeier, Eyke, and A. Fallah Tehrani. “On the VC Dimension of the Choquet Integral.” In Proceedings IPMU-2012 14th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Catania, Italy, 2012.
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Fallah Tehrani, A., et al. “Learning  Monotone Nonlinear Models Using the Choquet Integral.” Machine Learning, vol. 89, no. 1, 2012, pp. 183–211.
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Bustince, H., et al. “Grouping, Overlap and Generalized Bientropic Functions for Fuzzy Modeling of Pairwise Comparisons.” IEEE Transactions on Fuzzy Systems, vol. 20, no. 3, 2012, pp. 405–15.
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Fürnkranz, J., et al. “Preference-Based Reinforcement Learning: A Formal Framework and a Policy Iteration Algorithm.” Machine Learning, vol. 89, no. 1, 2012, pp. 123–56.
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Dembczynski, K., et al. “On Label Dependence and Loss  Minimization in Multi-Label Classification.” Machine Learning, vol. 88, no. 1–2, 2012, pp. 5–45.
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Shaker, Ammar, and Eyke Hüllermeier. “IBL Streams: A System for Instance-Based Classification and Regression on Data Streams.” Evolving Systems, vol. 3, no. 4, 2012, pp. 235–49.
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Dolorez Ruiz, M., and Eyke Hüllermeier. “A Formal and Empirical Analysis of the Fuzzy Gamma Rank Correlation Coefficient.” Information Sciences, vol. 206, 2012, pp. 1–17.
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Fallah Tehrani, A., et al. “Preference Learning Using the Choquet Integral: The Case of Multipartite Ranking.” IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, 2012, pp. 1102–13.
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