Publications by year
W. Waegeman, K. Dembczynski, E. Hüllermeier,
Multi-target prediction: A unifying view on problems and methods.
Data Mining, Knowledge Discovery, 33(2):293-324, 2019.
[ PDF ]
S. Henzgen, E. Hüllermeier.
Mining Rank Data.
IEEE/ACM Transactions on Knowledge Discovery from Data, 13(6), 2019.
I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse.
Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.
IEEE Computational Intelligence Magazine, 14(1):31-44, 2019.
V. Melnikov, E. Hüllermeier,
Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.
Proceedings ACML, Asian Conference on Machine Learning, Proceedings of Machine Learning Research, 101, 2019.
[ PDF ]
K. Brinker, E. Hüllermeier.
A Reduction of Label Ranking to Multiclass Classification.
Proceedings ECML/PKDD, European Conference on Machine Learning, Knowledge Discovery in Databases, Würzburg, Germany, 2019.
[ PDF ]
V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier.
Choice Functions Generated by Mallows and Plackett–Luce Relations.
New Mathematics, Natural Computation, 15(2):191-213, 2019.
M. Ahmadi Fahandar, E. Hüllermeier.
Feature Selection for Analogy-based Learning to Rank.
Proceedings DS 2019, 22nd International Conference on Discovery Science, Split, Croatia, pp. 279-289, Springer, 2019.
E. Hüllermeier, I. Couso, S. Destercke.
Learning from Imprecise Data: Adjustments of Optimistic, Pessimistic Variants.
Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, pp. 266-279, Compiegne, France, Springer, 2019.
V.L. Nguyen, S. Destercke, E. Hüllermeier.
Epistemic uncertainty sampling.
Proceedings DS 2019, 22nd International Conference on Discovery Science, Split, Croatia, pp. 72-86, Springer, 2019.
A. El Mesaoudi-Paul, E. Hüllermeier, R. Busa-Fekete.
Ranking Distributions based on Noisy Sorting.
Proceedings ICML 2018, 35th International Conference on Machine Learning, Stockholm, Sweden, pp. 3469-3477, 2018.
V.L. Nguyen, S. Destercke, M.H. Masson, E. Hüllermeier.
Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty.
Proceedings IJCAI 2018, 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 5089-5095, 2018.
F. Mohr, M. Wever, E. Hüllermeier.
Reduction Stumps for Multi-class Classification.
Proceedings IDA 2018, 17th International Symposium on Advances in Intelligent Data Analysis, Hertogenbosch, The Netherlands, pp. 225-237, 2018.
D. Schäfer, E. Hüllermeier.
Preference-Based Reinforcement Learning Using Dyad Ranking.
Proceedings DS 2018, 21st International Conference on Discovery Science, Limassol, Cyprus, pp. 161-175, 2018.
E. Loza Mencia, J. Fürnkranz, E. Hüllermeier, M. Rapp.
Learning Interpretable Rules for Multi-label Classification.
In: H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, M.A.J. van Gerven (eds.), Explainable, Interpretable Models in Computer Vision, Machine Learning, pp. 81-113, Springer, 2018.
F. Mohr, M. Wever, A. Faez, E. Hüllermeier.
Towards the Automated Composition of Machine Learning Services.
Proceedings SCC 2018, IEEE International Conference on Services Computing, San Francisco, CA, USA, pp. 241-244, 2018.
F. Mohr, M. Wever, E. Hüllermeier.
On-the-Fly Service Construction with Prototypes.
Proceedings SCC 2018, IEEE International Conference on Services Computing, San Francisco, CA, USA, pp. 225-232, 2018.
V. Melnikov, E. Hüllermeier.
On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis.
Machine Learning, 107(8-10):1537-1560, 2018.
F. Mohr, M. Wever, E. Hüllermeier,
ML-Plan: Automated machine learning via hierarchical planning.
Machine Learning, 107(8-10):1495-1515, 2018.
M. Hesse, J. Timmermann, E. Hüllermeier, A. Trächtler.
A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart.
Proceedings 4th International Conference on System-Integrated Intelligence, Hannover, Germany, 2018.
M. Wever, F. Mohr, E. Hüllermeier.
Ensembles of Evolved Nested Dichotomies for Classification.
Proc. GECCO 2018, The Genetic, Evolutionary Computation Conference, Kyoto, Japan, 2018.
D. Schäfer, E. Hüllermeier.
Dyad Ranking using Plackett-Luce Models based on Joint Feature Representations.
Machine Learning, 107(5):903-941, 2018.
M. Ahmadi Fahandar, E. Hüllermeier.
Learning to Rank based on Analogical Reasoning.
Proceedings AAAI 2018, 32nd AAAI Conference on Artificial Intelligence. New Orleans Riverside, USA, 2018.
M. Ahmadi Fahandar, E. Hüllermeier, I. Couso.
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening.
Proceedings ICML, 34th International Conference on Machine Learning, Sydney, Australia, 2017.
[ PDF ]
A. Shaker, W. Heldt, E. Hüllermeier.
Learning TSK Fuzzy Rules from Data Streams.
Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2017.
[ PDF ]
F. Mohr, T. Lettmann, E. Hüllermeier.
Planning with Independent Task Networks.
Proceedings KI, 40th German Conference on Artificial Intelligence, Dortmund, Germany, 2017.
[ PDF ]
M. Czech, E. Hüllermeier, M.C. Jakobs, H. Wehrheim.
Predicting Rankings of Software Verification Tools.
ESEC/FSE Workshops 2017, 3rd ACM SIGSOFT International Workshop on Software Analytics (SWAN 2017), Paderborn, Germany, 2017.
[ PDF ]
N. Seemann, M. Geierhos, M.L. Merten, D. Tophinke.
Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German.
LaTeCH-CLfL, Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 40-45, 2017.
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A. Shabani, A. Paul , R. Platon, E. Hüllermeier.
Predicting the Electricity Consumption of Buildings: An Improved CBR Approach.
Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, pp. 356-369, 2016.
[ PDF ]
K. Dembczynski, W. Kotlowski, W. Waegeman, R. Busa-Fekete, E. Hüllermeier.
Consistency of Probabilistic Classifier Trees.
Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy, pp. 511-526, 2016.
[ PDF ]
V. Melnikov, P. Gupta, B. Frick, D. Kaimann, E. Hüllermeier.
Pairwise versus Pointwise Ranking: A Case Study.
Schedae Informaticae, 25:73-83, 2016.
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S. Abiteboul et al.
Research Directions for Principles of Data Management (Abridged).
SIGMOD} Record, 45(4):5-17, 2016.
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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.
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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.
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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.
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Eyke Hüllermeier.
From knowledge-based to data-driven fuzzy modeling: Development, criticism, and alternative directions.
Informatik Spektrum, 38(6):500-509, 2015.
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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.
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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.
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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.
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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.
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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.
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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)
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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.
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M. Aggarwal, A. Fallah Tehrani, E. Hüllermeier.
Preference-based Learning of Ideal Solutions in TOPSIS-like Decision Models.
Journal of Multi-Criteria Decision Analysis, 22(3-4), 2014.
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S. Henzgen, M. Strickert, and E. Hüllermeier.
Visualization of Evolving Fuzzy Rule-Based Systems.
Evolving Systems, 5, 175-191, 2014.
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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.
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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.
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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.
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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.
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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.
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A. Shaker, R. Senge and E. Hüllermeier.
Evolving Fuzzy Pattern Trees for Binary Classification on Data Streams.
Information Sciences, 220:34-45, 2013.
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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.
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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 ]