Newspaper

    Recent Publications

    K. Jasinska, K. Dembczynski, R. Busa-Fekete, T. Klerx, E. Hüllermeier.
    Extreme F-Measure Maximization using Sparse Probability Estimates.
    Proceedings ICML, 33th International Conference on Machine Learning, 2016.
    PDF ]

    K. Pfannschmidt, E. Hüllermeier, S. Held, R. Neiger.
    Evaluating Tests in Medical Diagnosis: Combining Machine Learning with Game-Theoretical Concepts.
    Proceedings IPMU, 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part I, pp. 450-461, 2016.
    PDF ]

    V. Melnikov and E. Hüllermeier.
    Learning to Aggregate using Uninorms.
    Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2016.
    PDF ] 

    D. Schäfer and E. Hüllermeier.
    Plackett-Luce Networks for Dyad Ranking.
    Workshop LWDA, "Lernen, Wissen, Daten, Analysen", Potsdam, 2016. 
    PDF ]

    J. Fürnkranz and E. Hüllermeier.
    Preference Learning.
    In: C. Sammut and G.I. Webb (eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2016.
    PDF ]

    M. Riemenschneider, R. Senge, U. Neumann, E. Hüllermeier, 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(10), 2016.

    M. Leinweber, T. Fober, M. Strickert, L. Baumgärtner, G. Klebe, B. Freisleben, E. Hüllermeier.
    CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites.
    IEEE Transactions on Knowledge and Data Engineering, 28(6):1423-1434, 2016.

    B. Szörenyi, R. Busa-Fekete, A. Paul and E. Hüllermeier. 
    Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. 
    Proceedings NIPS 2015, Advances in Neural Information Processing Systems 28, pp. 604--612, 2015.
    PDF ]

    B. Szörenyi, R. Busa-Fekete, K. Dembczynski and E. Hüllermeier. 
    Online F-Measure Optimization. 
    Proceedings NIPS 2015, Advances in Neural Information Processing Systems 28, pp. 595--603, 2015.
    PDF ]

    Balázs Szörényi, Róbert Busa-Fekete, Paul Weng, Eyke Hüllermeier. 
    Qualitative Multi-Armed Bandits: A Quantile-Based Approach. 
    Proc. ICML 2015, International Conference on Machine Learning, pp. 1660-1668.
    PDF ]

    Dirk Schäfer, Eyke Hüllermeier. 
    Dyad Ranking Using A Bilinear Plackett-Luce Model. 
    Proc. ECML/PKDD 2015, European Conference on Machine Learning and Knowledge Discovery in Databases, Porto, Portugal, pp. 227-242, 2015. 
    PDF ]

    Eyke Hüllermeier, Weiwei Cheng. 
    Superset Learning Based on Generalized Loss Minimization. 
    Proc. ECML/PKDD 2015, European Conference on Machine Learning and Knowledge Discovery in Databases, Porto, Portugal, pp. 260-275, 2015. 
    PDF ]

    Sascha Henzgen, Eyke Hüllermeier. 
    Weighted Rank Correlation: A Flexible Approach Based on Fuzzy Order Relations. 
    Proc. ECML/PKDD 2015, European Conference on Machine Learning and Knowledge Discovery in Databases, Porto, Portugal, pp. 422-437, 2015. 
    PDF ]

    Eyke Hüllermeier. 
    Does machine learning need fuzzy logic? 
    Fuzzy Sets and Systems, 281:292-299, 2015.
    PDF ]

    Eyke Hüllermeier. 
    From knowledge-based to data-driven fuzzy modeling: Development, criticism, and alternative directions. 
    Informatik Spektrum, 38(6):500-509, 2015.
    PDF ]

    Santiago Garcia-Jimenez, Humberto Bustince, Eyke Hüllermeier, Radko Mesiar, Nikhil R. Pal, Ana Pradera. 
    Overlap Indices: Construction of and Application to Interpolative Fuzzy Systems. 
    IEEE Transactions on Fuzzy Systems, 23(4):1259-1273, 2015.

    Robin Senge, Eyke Hüllermeier. 
    Fast Fuzzy Pattern Tree Learning for Classification. 
    IEEE Transactions on Fuzzy Systems, 23(6):2024-2033, 2015. 

    Amira Abdel-Aziz, Eyke Hüllermeier. 
    Case Base Maintenance in Preference-Based CBR. 
    Proc. ICCBR 2015, 23rd International Conference on Case-Based Reasoning, pp. 1-14, Springer-Verlag, LNAI 9343, 2015.
    PDF ]

    W. Waegeman, K. Dembczynski, A. Jachnik, W. Cheng, and E. Hüllermeier.
    On the Bayes-Optimality of F-Measure Maximizers.
    Journal of Machine Learning Research, 15:3333-3388, 2015.
    [ PDF ]

    Adil Paul, Eyke Hüllermeier.
    A CBR Approach to the Angry Birds Game.
    Workshop Proceedings from ICCBR 2015, 23rd International Conference on Case-Based Reasoning, pp. 68-77.
    PDF 

    S. Lu and E. Hüllermeier.
    Locally weighted regression through data imprecisiation.
    In: F. Hoffmann and E. Hüllermeier (eds.) Proceedings 25. Workshop Computational Intelligence, pp. 97--104, KIT Scientific Publishing, 2015.
    PDF 

    S. Henzgen and E. Hüllermeier.
    Mining Rank Data.
    Proc. DS-2014, International Conference on Discovery Science, pp. 123-143, Bled, Slovenia, 2014. 
    PDF 

    E. Hüllermeier.
    Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization.

    International Journal of Approximate Reasoning, 55(7):1519-1534, 2014.
    [ PDF ]

    R. Busa-Fekete, B. Szörenyi, P. Weng, W. Cheng, E. Hüllermeier.
    Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm.
    Machine Learning, 97(3):327-351, 2014.
    [ PDF ]

    G. Krempl, I. Zliobaite, D. Brzezinski, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S. Sievi, M. Spiliopoulou, J. Stefanowski.
    Open challenges for data stream mining research.
    SIGKDD Explorations 16(1): 1-10 (2014)
    [ PDF ]

    A. Shaker and E. Hüllermeier.
    Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams: Experimental Design and Case Study.
    Neurocomputing, 150:250-264, 2015. 
    [ PDF ]

    S. Henzgen, M. Strickert, and E. Hüllermeier.
    Visualization of Evolving Fuzzy Rule-Based Systems.
    Evolving Systems, 5, 175-191, 2014. 
    [ PDF ]

    R. Busa-Fekete and E. Hüllermeier.
    A Survey of Preference-Based Online Learning with Bandit Algorithms.
    Proc. ALT-2014, 25th International Conference on Algorithmic Learning Theory, Bled, Slovenia, 2014.
     PDF ]

    R. Busa-Fekete, E. Hüllermeier, and B. Szörenyi.
    Preference-based Rank Elicitation using  Statistical Models: The Case of Mallows.
    Proc. ICML-2014, 31st International Conference on Machine Learning, Beijing, China, JMLR W&CP, 32(2):1071-1079, 2014.
     PDF ]

    R. Busa-Fekete, B. Szörenyi and E. Hüllermeier.
    PAC Rank Elcitation through Adaptive Sampling of Stochastic Pairwise Preferences .
    Proc. AAAI-2014, 28th National Conference on Artificial Intelligence, Québec, Canada, pp. 1701-1707, 2014.
     PDF ]

    A. Fallah Tehrani, M. Strickert and E. Hüllermeier.
    The Choquet Kernel for Monotone Data.
    Proc. ESANN-2014, European Symposium on Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, pp. 23-25, 2014.
     PDF ]

    A. Adel-Aziz, M. Strickert and E. Hüllermeier.
    Learning Solution Similarity in Preference-based CBR.
    Proc. ICCBR-2014, 22nd International Conference on Case-Based Reasoning, Cork, Ireland, 2014.
    PDF ]

    A. Shaker and E. Hüllermeier.
    Survival Analysis on Data Streams: Analyzing Temporal Events in Dynamically Changing Environmets .
    International Journal of Applied Mathematics and Computer Science, 24(1):199-212, 2014.
    [ Draft-PDF ]

    M. Mernberger, D. Moog, S. Stork, S. Zauner, U. Maier and E. Hüllermeier.
    Protein Sub-Cellular Localization Prediction for Special Compartments via Optimized Time Series Distances .
    Journal of Bioinformatics and Computational Biology, 12(1), 2014.
    Abstract ]

    R. Busa-Fekete, B. Szöreny, P. Weng, W. Cheng and E. Hüllermeier.
    Top-k Selection based on Adative Sampling of Noisy Preferences.
    Proc. ICML-13, 30th International Conference on Machine Learning (JMLR W&CP 28(3):1094-1102).
    Atlanta, USA, 2013.
    PDF ]

    A. Shaker, R. Senge and E. Hüllermeier.
    Evolving Fuzzy Pattern Trees for Binary Classification on Data Streams.
    Information Sciences, 220:34-45, 2013.
     PDF ]

    K. Dembczynski, A. Jachnik, W. Kotlowski, W. Waegeman and E. Hüllermeier.
    Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization.
    Proc. ICML-13, 30th International Conference on Machine Learning (JMLR W&CP 28(3):1130-1138).
    Atlanta, USA, 2013.
    PDF ]

    R. Senge, S. Bösner, K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff and E. Hüllermeier.
    Reliable Classification: Learning Classifiers that Distinguish Aleatoric and Epistemic Uncertainty.
    Information Sciences, 2013.
    [ Draft-PDF ] [ Online-Version ]

    W. Cheng and E. Hüllermeier.
    A Nearest Neighbor Approach to Label Ranking based on Generalized Labelwise Loss Minimization.
    Proc. M-PREF'13, 7th Multidisciplinary Workshop on Preference Handling.
    Beijing, China, 2013.
     PDF ]

    A. Fallah Tehrani and E. Hüllermeier.
    Ordinal Choquistic Regression. 
    Proc. EUSFLAT 2013, 8th International Conference of the European, Milano, Italy, 2013.
     PDF ]

    E. Hüllermeier and W. Cheng.
    Preference-based CBR: General Ideas and Basic Principles.
    Proc. IJCAI-2013, 23rd International Joint Conference on Artificial Intelligence.
    Beijing, China, pp. 3012-3016, AAAI Press, 2013.
    PDF ]

    M. Nasiri, T. Fober, R. Senge and E. Hüllermeier.
    Fuzzy Pattern Trees as an Alternative to Rule-based Fuzzy Systems: Knowledge-driven, Data-driven and Hybrid Modeling of Color Yield in Polyester Dyeing.
    Proc. IFSA World Congress.
    Edmonton, Canada, pp. 715-721, 2013.
    PDF ]

    A. Shaker and E. Hüllermeier.
    Event History Analysis on Data Streams: An Application to Earthquake Occurrence. 
    In: K. Krempl, I. Zliobaite, Y. Wang, G. Forman (eds.), Proc. RealStream 2013, 1st Int. Workshop on Real-World Challenges for Data Stream Mining.
    Prague, Czech Republic, pp. 38-41, 2013.
    PDF ] [   full proceedings ] 

    S. Henzgen, M. Strickert and E. Hüllermeier.
    Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems.
    Proc. CORES-2013, 8th International Conference on Computer Recognition Systems.
    Wroclaw, Poland, pp. 279-288, Springer, 2013.
    PDF ]

    A. Adel-Aziz, W. Cheng M. Strickert and E. Hüllermeier.
    Preference-based CBR: A Search-based Problem Solving Framework.
    Proc. ICCBR-2013, 21st International Conference on Case-Based Reasoning.
    Saratoga Springs, NY, USA, pp. 1-14, Springer (LNAI 7969), 2013.
    [ PDF ]

    A. Shaker and E. Hüllermeier.
    Recovery Analysis for Adaptive Learning from Non-stationary Data Streams.
    Proc. CORES-2013, 8th International Conference on Computer Recognition Systems.
    Wroclaw, Poland, pp. 289-298, Springer, 2013.
    [ PDF ]

    W. Cheng and E. Hüllermeier.
    Probability Estimation for Multiclass Classification based on Label Ranking.
    Proc. ECML/PKDD-2012, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, UK, September 2012.
    [ PDF ]

    R. Senge, Juan Jose del Coz and E. Hüllermeier.
    On the Problem of Error Propagation in Classifier Chains for Multi-Label Classication.
    L. Schmidt-Thieme and M. Spiliopoulou (eds.) Proc. GFKL-2012, 36th Annual Conference of the German Classification Society. Springer, 2013 (forthcoming).
    Draft-PDF ]

    W. Cheng, E. Hüllermeier, W. Waegeman and V. Welker.
    Label Ranking with Abstention based on Thresholded Probabilistic Models.
    NIPS-2012, 26th Annual Conference on Neural Information Processing Systems.
    Lake Tahoe, Nevada, US, 2012.
    [ PDF ]

    A. Fallah Tehrani, W. Cheng, K. Dembczynski and E. Hüllermeier.
    Learning Monotone Nonlinear Models using the Choquet Integral.
    Machine Learning, 89(1):183-211, 2012. DOI: 10.1007/s10994-012-5318-3
    [ Draft-PDF ] [ Publisher ]

    J. Fürnkranz, E. Hüllermeier, W. Cheng and S.H. Park
    Preference-Based Reinforcement Learning: A Formal framework and a Policy Iteration Algorithm.
    Machine Learning, 89(1):123-156, 2012. DOI: 10.1007/s10994-012-5313-8
    [ Draft-PDF ] [ Publisher ]

    A. Shaker and E. Hüllermeier.
    IBLStreams: A System for Classification and Regression on Data Streams.
    Evolving Systems, 2012 (forthcoming).
    Draft-PDF ]

    E. Hüllermeier and A. Fallah Tehrani.
    On the VC Dimension of the Choquet Integral.
    IPMU-2012, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems.
    Catania, Italy, 2012.
    [ PDF ]

    R. Senge, T. Fober, M. Nasiri and E. Hüllermeier.
    Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy Modellierung.
    at - Automatisierungstechnik, 60(10):622-629, 2012. 
    [ PDF ] [ Publisher ]

    K. Dembczynski, W. Waegeman, W. Cheng and E. Hüllermeier.
    An Exact Algorithm for F-Measure Maximization.
    NIPS-2011, 25th Annual Conference on Neural Information Processing Systems.
    Granada, Spain, 2011.
    [ PDF ]

    E. Hüllermeier and A. Fallah Tehrani.
    Efficient Learning of Classifiers based on the 2-additive Choquet Integral.
    In: C. Moewes and A. Nürnberger (eds). Computational Intelligence in Intelligent Data Analysis. Studies in Computational Intelligence, pp. 17-30, Springer.
    [ Draft-PDF ] [ Publisher ]

    E.Hüllermeier.
    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, Studies in Fuzziness and Soft Computing (vol 271), pp. 123-135, Springer.
    [ Draft-PDF ] [ Publisher ]

    M. Dolores Ruiz and E.Hüllermeier.
    A Formal and Empirical Analysis of the Fuzzy Gamma Rank Correlation Coefficient.
    Information Sciences (to appear), 2012.
    [ Draft-PDF ] [ Publisher ]

    A. Fallah Tehrani, W. Cheng and E. Hüllermeier. Preference Learning using the Choquet Integral: The Case of Multipartite Ranking.
    IEEE Transactions on Fuzzy Systems, 2012 (forthcoming).
    Draft-PDF ]
     
    Eyke Hüllermeier, Maria Rifqi, Sascha Henzgen and Robin Senge.
    Comparing Fuzzy Partitions: A Generalization of the Rand Index and Related Measures.
    IEEE Transactions on Fuzzy Systems, 20(3):546-556, 2012.
    [ Draft-PDF ]

    A. Fallah Tehrani, W. Cheng, K. Dembczynski and E. Hüllermeier.
    Learning Monotone Nonlinear Models using the Choquet Integral.
    Proc. ECML/PKDD-2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
    Athens, Greece, September 2011.
    [ PDF ]

    W. Kotlowski, K. Dembczynski and E. Hüllermeier.
    Bipartite Ranking through Minimization of Univariate Loss.
    Proc. ICML-2011, 28th International Conference on Machine Learning.
    Washington, USA, June 2011.
    [ PDF ]

    W. Cheng, J. Fürnkranz, E. Hüllermeier and S.H. Park
    Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning.
    Proc. ECML/PKDD-2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
    Athens, Greece, September 2011.
    [ PDF ]

    A. Fallah Tehrani, W. Cheng and E. Hüllermeier.
    Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral.
    Proc. Eusflat-LFA 2011, 7th International Conference of the European Society for Fuzzy Logic and Technology.
    Aix-les-Bains, France, July 2011.
    [ PDF ] [ Slides ]

    E. Lughofer and E. Hüllermeier.
    On-line Redundancy Deletion in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure.
    Proc. Eusflat-LFA 2011, 7th International Conference of the European Society for Fuzzy Logic and Technology.
    Aix-les-Bains, France, July 2011.
    [ PDF ]

    M. Nasiri, E. Hüllermeier, R. Senge and E. Lughofer.
    Comparing Methods for Knowledge-Driven and Data-Driven Fuzzy Modeling: A Case Study in Textile Industry.
    Proc. IFSA-2011, World Congress of the International Fuzzy Systems Association.
    Surabaya and Bali Island, Indonesia, June 2011.
    [ PDF ]

    E. Hüllermeier and P. Schlegel.
    Preference-Based CBR: First Steps Toward a Methodological Framework.
    Proc. ICCBR-2011, 19th International Conference on Case-Based Reasoning.
    London, September 2011.
    [ PDF ] [ Slides ]

    E. Hüllermeier.
    Fuzzy Sets in Machine Learning and Data Mining.
    Applied Soft Computing Journal, 11:1493-1505, 2011.
    [ Draft-PDF ]

    M. Mernberger, G. Klebe and E. Hüllermeier.
    SEGA: Semi-Global Graph Alignment for Structure-based Protein Comparison.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(5):1330-1343, 2011.
    [ Draft-PDF ]

    T. Fober, S. Glinca, G. Klebe and E. Hüllermeier.
    Superposition and Alignment of Labeled Point Clouds.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6):1653-1666, 2011. 
    [ Draft-PDF ]

    R. Senge and E. Hüllermeier.
    Top-Down Induction of Fuzzy Pattern Trees.
    IEEE Transactions on Fuzzy Systems (to appear).
    [ Draft-PDF ]

    W. Cheng, M. Rademaker, B. De Beats and E. Hüllermeier.
    Predicting Partial Orders: Ranking with Abstention.
    Proc. ECML/PKDD 2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
    Barcelona, Spain, September 2010.
    [ Draft-PDF ]

    K. Dembczynski, W. Waegeman, W. Cheng and E. Hüllermeier.
    Regret Analysis for Performance Metrics in Multi-Label Classication: The Case of Hamming and Subset Zero-One Loss.
    Proc. ECML/PKDD 2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
    Barcelona, Spain, September 2010.
    [ Draft-PDF ]

    W. Cheng, K. Dembczynski and E. Hüllermeier.
    Graded Multi-Label Classification: The Ordinal Case.
    Proc. ICML-2010, International Conference on Machine Learning.
    Haifa, Israel, June 2010.
    [ PDF ]

    W. Cheng, K. Dembczynski and E. Hüllermeier.
    Label Ranking based on the Placket-Luce Model.
    Proc. ICML-2010, International Conference on Machine Learning.
    Haifa, Israel, June 2010.
    [ PDF ]

    K. Dembczynski, W. Cheng and E. Hüllermeier.
    Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains.
    Proc. ICML-2010, International Conference on Machine Learning.
    Haifa, Israel, June 2010.
    [ PDF ]

    K. Dembczynski, W. Waegeman, W. Cheng and E. Hüllermeier.
    On Label Dependence in Multi-Label Classification.
    Proc. MLD 2010, 2nd Int. Workshop "Learning from Multi-Label Data".
    Haifa, Israel, June 2010.
    [ PDF ]

    H.W. Koh and E. Hüllermeier.
    Mining Gradual Dependencies based on Fuzzy Rank Correlation.
    Proc. SMPS 2010, 5th Int. Conf. on Soft Methods in Probability and Statistics.
    Oviedo/Mieres (Asturias), Spain, October 2010.
    [ PDF ]

    R. Senge and E. Hüllermeier.
    Pattern Trees for Regression and Fuzzy Systems Modeling.
    Proc. WCCI-2010, World Congress on Computational Intelligence.
    Barcelona, July 2010.
    [ PDF ] [ Slides ]

    T. Fober and E. Hüllermeier.
    Similarity Measures for Protein Structures based on Fuzzy Histogram Comparison.
    Proc. WCCI-2010, World Congress on Computational Intelligence.
    Barcelona, July 2010.
    [ PDF ]

    W. Cheng and E. Hüllermeier.
    Combining instance-based learning and logistic regression for multilabel classification.
    Machine Learning 76(2-3):211-235, 2009.
    [ Draft-PDF ]

    T. Fober, M. Mernberger, R. Moritz and E. Hüllermeier.
    Graph-Kernels for the Comparative Analysis of Protein Active Sites.
    Proc. GCB-2009, German Conference on Bioinformatics.
    Halle (Saale), Germany, September 2009.
    [ Draft-PDF ]

    T. Fober, M. Mernberger, and E. Hüllermeier.
    Evolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules.
    Bioinformatics (to appear).
    [ Draft-PDF supplementary ]

    E. Hüllermeier and M. Rifqi.
    A Fuzzy Variant of the Rand Index for Comparing Clustering Structures.
    Proceedings IFSA/EUSFLAT-2009, World Congress of the Fuzzy Systems Association, Lisbon, Portugal, 2009.
    [ Draft-PDF ]

    E. Hüllermeier and S. Vanderlooy.
    Why Fuzzy Decision Trees are Good Rankers
    IEEE Transactions on Fuzzy Systems 17(5), 2009.
    [ Draft-PDF ]

    E. Hüllermeier and S. Vanderlooy.
    Combining Predictions in Pairwise Classifiation: An Optimal Adaptive Voting Strategy and Its Relation to Weighted Voting
    Pattern Recognition 43(1):128-142, 2010.
    [ Draft-PDF ]

    W. Cheng, J. Hühn, and E. Hüllermeier.
    Decision Tree and Instance-Based Learning for Label Ranking.
    Proc. ICML-09, International Conference on Machine Learning.
    Montreal, Canada, June 2009.
    [ PDF ]

    J. Hühn and E. Hüllermeier.
    FURIA: An Algorithm for Unordered Fuzzy Rule Induction.
    Data Mining and Knowledge Discovery 19:293-319, 2009.
    [ Draft-PDF | Software ]

    N. Weskamp, E. Hüllermeier, and G. Klebe.
    Merging chemical and biological space: Structural mapping of enzyme binding pocket space.
    Proteins: Structure, Function and Bioinformatics 76(2):317-30, 2009.

    I. Boukhris, Z. Elouedi, T. Fober, M. Mernberger and E. Hüllermeier.
    Similarity Analysis of Protein Binding Sites: A Generalization of the Maximum Common Subgraph Measure Based on Quasi-Clique Detection.
    Proc. ISDA-2009, 9th International Conference on Intelligent Systems Design and Applications.
    Pisa, Italy, 2009.
    [ Draft-PDF ]

    W. Cheng and E. Hüllermeier
    A New Instance-Based Label Ranking Approach using the Mallows Model
    LNCS 5551 Advances in Neural Networks: 707-716, Springer
    The 6th International Symposium on Neural Networks
    Wuhan, China, May 2009
    [ Draft-PDF ]

    J. Hühn and E. Hüllermeier.
    Is an ordinal class structure useful in classifier learning?
    Int. Journal of Data Mining, Modelling and Management 1(1):45–67, 2008.
    [ Draft-PDF ]

    Y. Yi, T. Fober and E. Hüllermeier.
    Fuzzy Operator Trees for Modeling Rating Functions
    Int. Journal of Computational Intelligence and Applications 8(4):413-428, 2009.
    [ Draft-PDF ]

    T. Fober, M. Mernberger, and E. Hüllermeier.
    Evolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules.
    Proc. GCB-2008, German Conference on Bioinformatics.
    Dresden, Germany, September 2008.
    [ Draft-PDF ]

    S. Vanderlooy and E. Hüllermeier.
    A Critical Analysis of Variants of the AUC.
    Machine Learning 72:247-272, 2008.
    [ PDF ]

    E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker.
    Label Ranking by Learning Pairwise Preferences.
    Artificial Intelligence 172:1897-1917, 2008.
    [ Draft-PDF ]

    J. Fürnkranz, E. Hüllermeier, E. Mencia, and K. Brinker.
    Multilabel Classification via Calibrated Label Ranking.
    Machine Learning 73(2):133-153, 2008.
    [ Draft-PDF ]

    W. Cheng and E. Hüllermeier.
    Learning Similarity Functions from Qualitative Feedback.
    Proc. ECCBR-2008, 9th European Conference on Case-Based Reasoning.
    Trier, Germany, September 2008.
    [ Draft-PDF ]

    E. Hüllermeier, I. Vladimirskiy, B. Prados Suarez, and E. Stauch.
    Supporting Case-Based Retrieval by Similarity Skylines: Basic Concepts ad Extensions.
    Proc. ECCBR-2008, 9th European Conference on Case-Based Reasoning.
    Trier, Germany, September 2008.
    [ Draft-PDF ]

    J. Hühn and E. Hüllermeier
    FR3: A Fuzzy Rule Learner for Inducing Reliable Classifiers.
    IEEE Transactions on Fuzzy Systems 17(1):138-149, 2009.
    [ Draft-PDF | Software ]

    E. Hüllermeier and J. Fürnkranz.
    On Minimizing the Position Error in Label Ranking.
    Proc. ECML-07, 17th European Conference on Machine Learning.
    Warsaw, Poland, September 2007.
    [ Draft-PDF ]

    J.N. Sulzmann, J. Fürnkranz, and E. Hüllermeier.
    On Pairwise Naive Bayes Classifiers.
    Proc. ECML-07, 17th European Conference on Machine Learning.
    Warsaw, Poland, September 2007.
    [ Draft-PDF ]

    E. Hüllermeier and K. Brinker.
    Learning Valued Preference Structures for Solving Classification Problems.
    Fuzzy Sets and Systems 159(18):2337-2352, 2008.
    [ Draft-PDF ]

    E. Hüllermeier.
    Credible Case-Based Inference Using Similarity Profiles.
    IEEE Transactions on Knowledge & Data Engineering 19(5):847-858, 2007.
    [ Draft-PDF ]

    N. Weskamp, E. Hüllermeier, D. Kuhn, and G. Klebe.
    Multiple Graph Alignment for the Structural Analysis of Protein Active Sites.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(2):310-320, 2007.
    [ Draft-PDF ]

    J. Beringer and E. Hüllermeier.
    Efficient Instance-Based Learning on Data Streams.
    Intelligent Data Analysis 11(6):627-650, 2007.
    [ Draft-PDF ]

    K. Brinker and E. Hüllermeier.
    Label Ranking in Case-Based Reasoning.
    Proc. ICCBR-07, 7th International Conference on Case-Based Reasoning.
    Belfast, Northern Ireland, August 2007.
    [ Draft-PDF ]

    J. Beringer and E. Hüllermeier.
    Adaptive Optimization of the Number of Clusters in Fuzzy Clustering
    Proc. Fuzz-IEEE-07, IEEE International Conference on Fuzzy Systems.
    London, July 2007.
    [ Draft-PDF ]

    D. Kuhn, N. Weskamp, S. Schmitt, E. Hüllermeier, and G. Klebe.
    From the Similarity Analysis of Protein Cavities to the Functional Classification of Protein Families Using CavBase.
    Journal of Molecular Biology, 359(4):1023-1044, 2006.

    E. Hüllermeier and Y. Yi.
    In Defense of Fuzzy Association Analysis
    IEEE Transactions on Systems, Man, and Cybernetric - Part B, 37(4): 1039-1043, 2007.
    [ Draft-PDF ]

    E. Hüllermeier and J. Beringer.
    Learning from Ambiguously Labeled Examples
    Intelligent Data Analysis 10(5):419-440, 2006.
    [ Draft-PDF ]

    D. Dubois and E. Hüllermeier.
    Comparing Probability Measures Using Possibility Theory: A Notion of Relative Peakedness.
    International Journal of Approximate Reasoning 45(2):364-385, 2007.
    [ Draft-PDF ]

    K. Brinker and E. Hüllermeier.
    Case-Based Label Ranking.
    Proceedings ECML-06, 17th European Conference on Machine Learning
    Berlin, Germany, Sept 2006.
    [ Draft-PDF ]

    K. Brinker and E. Hüllermeier.
    Case-Based Multilabel Ranking.
    Proceedings IJCAI-07, 20th International Joint Conference on Artificial Intelligence
    Hyderabad, India, Feb 2007.
    [ Draft-PDF ]

    K. Brinker, E. Hüllermeier, and J. Fürnkranz.
    A Unified Model for Multilabel Classification and Ranking.
    Proceedings ECAI-06, 17th European Conference on Artificial Intelligence
    Riva del Garda, Italy, Aug/Sept 2006.
    [ Draft-PDF ]

    D. Dubois, E. Hüllermeier, and H. Prade.
    A Systematic Approach to the Assessment of Fuzzy Association Rules.
    Data Mining and Knowledge Discovery, 13(2): 167-192, 2006.
    [ Draft-PDF ]

    E. Hüllermeier and J. Beringer.
    Learning from Ambiguously Labeled Examples.
    Proceedings IDA-05, 6th International Symposium on Intelligent Data Analysis
    Madrid, Spain, September 2005.
    [ Draft-PDF ]

    R. Balasubramaniyan, E. Hüllermeier, N. Weskamp, and Jörg Kämper.
    Clustering of gene expression data using a local shape-based similarity measure.
    Bioinformatics, 21(7):1069–1077, 2005.

    J. Beringer and E. Hüllermeier.
    Fuzzy Clustering of Parallel Data Streams.
    In: J. Valente de Oliveira and W. Pedrycz (eds.), Advances in Fuzzy Clustering and Its Application, pp. 333-352, John Wiley and Sons, 2007.
    [ Draft-PDF ]

    Yu Yi and E. Hüllermeier.
    Learning Complexity-Bounded Rule-Based Classifiers by Combining Association Analysis and Genetic Algorithms.
    Proceedings EUSFLAT-2005, Barcelona, Spain, 2005.
    [ Draft-PDF ]

    E. Hüllermeier and J. Fürnkranz.
    Learning Label Preferences: Ranking Error versus Position Error.
    Proceedings IDA-05, 6th International Symposium on Intelligent Data Analysis
    Madrid, Spain, September 2005.
    [ Draft-PDF ]

    E. Hüllermeier.
    Cho-k-NN: A Method for Combining Interacting Pieces of Evidence in Case-Based Learning.
    Proceedings IJCAI-05, 19th International Joint Conference on Artificial Intelligence, pp 3-8.
    Edinburgh, Scotland, July/August 2005.
    [ Draft-PDF ]

    J. Beringer and E. Hüllermeier.
    Online-Clustering of Parallel Data Streams.
    Data and Knowledge Engineering 58(2), 180-204, 2006.
    [ Draft-PDF ]

    E. Hüllermeier.
    Fuzzy-Methods in Machine Learning and Data Mining: Status and Prospects.
    Fuzzy Sets and Systems 156(3), 387-407, 2005.
    [ Draft-PDF ]

    D. Dubois and E. Hüllermeier.
    A Notion of Comparative Probabilistic Entropy based on the Possibilistic Specificity Ordering.
    Proceedings ECSQARU-2005, 8. European Conferences on Symbolic and Quantitative Approaches to Reasoning with Uncertainty.
    Valencia, Spain, July 2005.
    [ Draft-PDF ]

    E. Hüllermeier.
    Instance-Based Prediction with Guaranteed Confidence.
    Proceedings ECAI-2004, 16th European Conference on Artificial Intelligence,
    Valencia, Spain, August 2004.
    [ PDF ]

    E. Hüllermeier and J. Fürnkranz.
    Ranking by Pairwise Comparison: A Note on Risk Minimization.
    Proceedings FUZZ-IEEE-04, IEEE International Conference on Fuzzy Systems.
    Budapest, Hungary, July 2004.
    [ PDF ]

    E. Hüllermeier and J. Fürnkranz.
    Comparison of Ranking Procedures in Pairwise Preference Learning.
    IPMU-04, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems.
    Perugua, Italy, 2004.
    [ PDF ]

    E. Hüllermeier and J. Fürnkranz (eds).
    Preference Learning: Models, Methods, Applications.
    Technical Report TR-2003-14, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, May 2003.
    Proceedings of the Workshop held as part of KI-2003, Hamburg, September 2003.
    [ PDF ]

    N. Weskamp, D. Kuhn, E. Hüllermeier and G. Klebe.
    Efficient Similarity Search in Protein Structure Databases: Improving Clique-Detection through Clique-Hashing.
    Proceedings GCB - 2003, German Conference on Bioinformatics, Munich, October 2003.
    [ PDF ]

    E. Hüllermeier.
    Possibilistic Instance-Based Learning.
    Artificial Intelligence, Volume 148, Issues 1-2, Pages 335-383, April 2003.
    [ Draft-PDF ]

    D. Dubois, E. Hüllermeier and H. Prade.
    A Note on Quality Measures for Fuzzy Association Rules.
    Proceedings IFSA-03, 10th International Fuzzy Systems Association World Congress,
    Lecture Notes in Artificial Intelligence, number 2715, pages 677-648, Springer-Verlag, Istambul, July 2003.
    [ Draft-PDF ]

    J. Fürnkranz and E. Hüllermeier.
    Pairwise Preference Learning and Ranking.
    Technical Report TR-2003-14, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, May 2003.
    [ PDF ]

    E. Hüllermeier
    Numerical methods for Fuzzy Initial Value problems.
    Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems, 7(5):439-461, 1999.
    [ PDF ]

    E. Hüllermeier
    A New Approach to Modelling and Simulation of Uncertain Dynamical Systems.
    Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems, 5(2):117-137, 1997.
    [ PDF ]