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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.
PDF ]

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. 
PDF ]

S. Abiteboul et al.
Research Directions for Principles of Data Management (Abridged).
SIGMOD} Record, 45(4):5-17, 2016.
PDF ]

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.
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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.
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J. Beringer and E. Hüllermeier.
Efficient Instance-Based Learning on Data Streams.
Intelligent Data Analysis 11(6):627-650, 2007.
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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.
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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.
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E. Hüllermeier and J. Beringer.
Learning from Ambiguously Labeled Examples
Intelligent Data Analysis 10(5):419-440, 2006.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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 ]

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