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405 Publications


2014 | Journal Article | LibreCat-ID: 10314
Busa-Fekete, R., Szörényi, B., Weng, P., Cheng, W., & Hüllermeier, E. (2014). Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.
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2014 | Journal Article | LibreCat-ID: 16064
Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning, 55(7), 1519–1534.
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2014 | Journal Article | LibreCat-ID: 16069
Henzgen, S., Strickert, M., & Hüllermeier, E. (2014). Visualization of evolving fuzzy-rule-based systems. Evolving Systems, 5, 175–191.
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2014 | Journal Article | LibreCat-ID: 16083
Donner-Banzhoff, N., Haasenritter, J., Hüllermeier, E., Viniol, A., Bösner, S., & Becker, A. (2014). The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology, 2(67), 124–132.
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2014 | Conference Paper | LibreCat-ID: 10295
Fürnkranz, J., Hüllermeier, E., Rudin, C., Slowinski, R., & Sanner, S. (2014). Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports (Vol. 4, pp. 1–27).
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2014 | Journal Article | LibreCat-ID: 10308
Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1519–1534.
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2014 | Journal Article | LibreCat-ID: 10310
Strickert, M., Bunte, K., Schleif, F.-M., & Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.
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2014 | Journal Article | LibreCat-ID: 10315
Montanés, E., Senge, R., Barranquero, J., Quevedo, J. R., Del Coz, J. J., & Hüllermeier, E. (2014). Dependent binary relevance models for multi-label classification. Pattern Recognition, 47(3), 1494–1508.
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2014 | Journal Article | LibreCat-ID: 16046
Agarwal, M., Fallah Tehrani, A., & Hüllermeier, E. (2014). Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis, 22(3–4).
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2014 | Journal Article | LibreCat-ID: 16060
Krotzky, T., Fober, T., Hüllermeier, E., & Klebe, G. (2014). Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 11(5), 878–890.
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2014 | Journal Article | LibreCat-ID: 16077
Busa-Fekete, R., Szörenyi, B., Weng, P., Cheng, W., & Hüllermeier, E. (2014). Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.
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2014 | Journal Article | LibreCat-ID: 10296
Shaker, A., & Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Applied Mathematics and Computer Science, 24(1), 199–212.
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2014 | Journal Article | LibreCat-ID: 10309
Hüllermeier, E. (2014). Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1609–1613.
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2014 | Journal Article | LibreCat-ID: 10311
Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., & Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
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2014 | Journal Article | LibreCat-ID: 10316
Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V., … Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.
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2014 | Conference Paper | LibreCat-ID: 10253
Schäfer, D., & Hüllermeier, E. (2014). Dyad Ranking Using A Bilinear Plackett-Luce Model. In Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany (pp. 32–33).
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2014 | Journal Article | LibreCat-ID: 16078
Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V., … Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.
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2014 | Journal Article | LibreCat-ID: 16080
Shaker, A., & Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science, 24(1), 199–212.
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2014 | Conference Paper | LibreCat-ID: 457
Jungmann, A., Mohr, F., & Kleinjohann, B. (2014). Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. In Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA) (pp. 105–112). https://doi.org/10.1109/SOCA.2014.48
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2013 | Conference Paper | LibreCat-ID: 13190
Shaker, A., & Hüllermeier, E. (2013). Recovery analysis for adaptive learning from non-stationary data streams. In R. Burduk, K. Jackowski, M. Kurzynski, W. Wozniak, & A. Zolnierek (Eds.), in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland (pp. 289–298). Springer.
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2013 | Conference Paper | LibreCat-ID: 13119
Henzgen, S., Strickert, M., & Hüllermeier, E. (2013). Rule chains for visualizing evolving fuzzy rule-based systems. In R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, & A. Zolnierek (Eds.), in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland (pp. 279–288). Springer.
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2013 | Journal Article | LibreCat-ID: 16081
Bösner, S., Bönisch, K., Haasenritter , J., Schlegel, P., Hüllermeier, E., & Donner-Banzhoff, N. (2013). 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, 14(1), 154–162.
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2013 | Journal Article | LibreCat-ID: 16086
Haasenritter, J., Viniol, A., Becker, A., Bösner, S., Hüllermeier, E., Senge, R., & Donner-Banzhoff, N. (2013). Diagnose im Kontext - eine erweiterte Perspektive. Zeitschrift Für Evidenz, Fortbildung Und Qualität Im Gesundheitswesen (ZEFQ), 107, 585–591.
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2013 | Conference Paper | LibreCat-ID: 15759
Cheng, W., & Hüllermeier, E. (2013). A nearest neigbor approach to label ranking based on generalized labelwise loss minimization. In In Proceedings M-PREF`13, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China.
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2013 | Conference Paper | LibreCat-ID: 15761
Senge, R., del Coz, J. J., & Hüllermeier, E. (2013). On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery. In L. Schmidt-Thieme & M. Spiliopoulou (Eds.), In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany . Springer.
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2013 | Conference Paper | LibreCat-ID: 13115
Szarvas, G., Busa-Fekete, R., & Hüllermeier, E. (2013). Learning to rank lexical substitutions. In In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA.
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2013 | Conference Paper | LibreCat-ID: 15755
Busa-Fekete, R., Fober, T., & Hüllermeier, E. (2013). Preference-based evolutionary optimization using generalized racing algorithms. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany (pp. 237–246). KIT Scientific Publishing.
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2013 | Journal Article | LibreCat-ID: 16044
Heider, D., Senge, R., Cheng, W., & Hüllermeier, E. (2013). Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction. Bioinformatics, 29(16), 1946–1952.
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2013 | Conference Paper | LibreCat-ID: 485
Mohr, F., & Kleine Büning, H. (2013). Semi-Automated Software Composition Through Generated Components. In Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS) (pp. 676–680). https://doi.org/10.1145/2539150.2539235
LibreCat | Files available | DOI
 

2013 | Conference Paper | LibreCat-ID: 13116
Dembczynski, K., Jachnik, A., Kotlowski, W., Waegeman, W., & Hüllermeier, E. (2013). Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization. In S. Dasgupta & D. McAllester (Eds.), in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA (pp. 1130–1138).
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2013 | Conference Paper | LibreCat-ID: 15756
Henzgen, S., & Hüllermeier, E. (2013). Weighted rank correlation measures based on fuzzy order relations. In F. Hoffmann & E. Hüllermeier (Eds.), in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany (pp. 227–236). KIT Scientific Publishing.
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2013 | Conference Paper | LibreCat-ID: 15763
Fober, T., Klebe, G., & Hüllermeier, E. (2013). Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics, Algorithms from and for Nature and Life. In B. Lausen, D. Van den Poel, & A. Ultsch (Eds.), In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany (pp. 279–286). Springer.
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2013 | Conference Paper | LibreCat-ID: 15112
Fallah Tehrani, A., & Hüllermeier, E. (2013). Ordinal Choquistic regression . In J. Montero, G. Pasi, & D. Ciucci (Eds.), in Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy. Atlantis Press.
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2013 | Conference Paper | LibreCat-ID: 13117
Busa-Fekete, R., Szoreny, B., Weng, P., Cheng, W., & Hüllermeier, E. (2013). Top-k selection based on adaptive sampling of noisy preferences. In S. Dasgupta & D. McAllester (Eds.), in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA (pp. 1094–1102).
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2013 | Conference Paper | LibreCat-ID: 15113
Nasiri, N., Fober, T., Senge, R., & Hüllermeier, E. (2013). 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 in Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada (pp. 715–721).
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2013 | Conference Paper | LibreCat-ID: 15752
Cheng, W., Henzgen, S., & Hüllermeier, E. (2013). Labelwise versus pairwise decomposition in label ranking. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany (pp. 129–136).
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2013 | Conference Paper | LibreCat-ID: 15757
Weng, P., Busa-Fekete, R., & Hüllermeier, E. (2013). Interactive Q-learning with ordinal rewards and unreliable tutor. In In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague.
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2013 | Journal Article | LibreCat-ID: 16123
Shaker, A., Senge, R., & Hüllermeier, E. (2013). Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences, 220, 34–45.
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2013 | Conference Paper | LibreCat-ID: 13118
Hüllermeier, E., & Cheng, W. (2013). Preference-based CBR: General ideas and basic principles. In F. Rossi (Ed.), in Proceedings IJCAI-13, 23rd international Joint Conference on Artificial Intelligence, Beijing, China (pp. 3012–3016). AAAI Press.
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2013 | Conference Paper | LibreCat-ID: 15753
Senge, R., del Coz, J., & Hüllermeier, E. (2013). Rectifying classifier chains for multi-label classification, Bamberg, Germany. In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany (pp. 151–158).
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2013 | Conference Paper | LibreCat-ID: 15758
Busa-Fekete, R., Szörenyi, B., Weng, P., & Hüllermeier, E. (2013). Preference-based evolutionary direct policy search. In In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague.
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2013 | Conference Paper | LibreCat-ID: 15760
Shaker, A., & Hüllermeier, E. (2013). Event history analysis on data streams: An application to earthquake occurence. In G. Krempl, I. Zliobaite, Y. Wang, & G. Forman (Eds.), In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic (pp. 38–41).
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2013 | Conference Paper | LibreCat-ID: 495
Mohr, F., Lettmann, T., & Kleine Büning, H. (2013). Reducing Nondeterminism in Automated Service Composition. In Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA) (pp. 154–161). https://doi.org/10.1109/SOCA.2013.25
LibreCat | Files available | DOI
 

2012 | Book Chapter | LibreCat-ID: 10153
Hüllermeier, E. (2012). Fuzzy rules in data mining: From fuzzy associations to gradual dependencies. In E. Trillas, P. P. Bonissone, L. Magdalena, & J. Kacprzyk (Eds.), Combining Experimentation and Theory (Vol. 271, pp. 123–135). Springer.
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2012 | Journal Article | LibreCat-ID: 16093
Hüllermeier, E., Rifqi, M., Henzgen, S., & Senge, R. (2012). Comparing fuzzy partitions: A generalization of the Rand index and related measures. IEEE Transactions on Fuzzy Systems, 20(3), 546–556.
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2012 | Conference Paper | LibreCat-ID: 15754
Bräuning, M., & Hüllermeier, E. (2012). Learning conditional lexicographic preference trees. In In Workshops on Preference Learning at ECAI, European Conference on Artiticial intelligence, Montpellier, France.
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2012 | Conference Paper | LibreCat-ID: 13191
Cheng, W., & Hüllermeier, E. (2012). Probability estimation for mulit-class classification based on label ranking. In Proceedings ECML/PKDD-2012, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, UK.
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2012 | Journal Article | LibreCat-ID: 16087
Fürnkranz, J., Hüllermeier, E., Cheng, W., & Park, S. H. (2012). Preference-based reinforcement learning: A formal framework and a policy iteration algorithm. Machine Learning, 89(1), 123–156.
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2012 | Journal Article | LibreCat-ID: 16094
Senge, R., Fober, T., Nasiri, N., & Hüllermeier, E. (2012). Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung. At-Atomatisierungstechnik, 60(10), 622–629.
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2012 | Conference Paper | LibreCat-ID: 13192
Dembczynski, K., Kotlowski, W., & Hüllermeier, E. (2012). Consistent multilabel ranking through univariate loss minimization. In J. Langford & J. Pineau (Eds.), in Proceedings ICML-2012,  International Conference on Machine Learning, Edinburgh, Scotland.
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