Survival analysis on data streams: Analyzing temporal events in dynamically changing environments
A. Shaker, E. Hüllermeier, Applied Mathematics and Computer Science 24 (2014) 199–212.

Ausgewählte Beiträge des GMA-Fachausschusses 5.14
F. Hoffmann, E. Hüllermeier, A. Kroll, Computational Intelligence Automatisierungstechnik 62 (2014) 685–686.

Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track
T. Calders, F. Esposito, E. Hüllermeier, R. Meo, Data Min. Knowledge Discovery 28 (2014) 1129–1133.

Visualization of evolving fuzzy rule-based systems
S. Henzgen, M. Strickert, E. Hüllermeier, Evolving Systems 5 (2014) 175–191.

Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
E. Hüllermeier, Int. J. Approx. Reasoning 55 (2014) 1519–1534.

Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
E. Hüllermeier, Int. J. Approx. Reasoning 55 (2014) 1609–1613.

Correlation-based embedding of pairwise score data
M. Strickert, K. Bunte, F.-M. Schleif, E. Hüllermeier, Neurocomputing 141 (2014) 97–109.

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty
R. Senge, S. Bösner, K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff, E. Hüllermeier, Information Sciences 255 (2014) 16–29.

Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances
M. Mernberger, M. Moog, S. Stork, S. Zauner, U.G. Maier, E. Hüllermeier, J. Bioinformatics and Computational Biology 12 (2014).

Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track
T. Calders, F. Esposito, E. Hüllermeier, R. Meo, Machine Learning 97 (2014) 1–3.

Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm
R. Busa-Fekete, B. Szörényi, P. Weng, W. Cheng, E. Hüllermeier, Machine Learning 97 (2014) 327–351.

Dependent binary relevance models for multi-label classification
E. Montanés, R. Senge, J. Barranquero, J.R. Quevedo, J.J. Del Coz, E. Hüllermeier, Pattern Recognition 47 (2014) 1494–1508.

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

Extended Graph-Based Models for Enhanced Similarity Search in Cavbase
T. Krotzky, T. Fober, E. Hüllermeier, G. Klebe, IEEE/ACM Trans. Comput. Biology Bioinform. 11 (2014) 878–890.

Identification of Functionally Releated Enzymes by Learning to Rank Methods
M. Stock, T. Fober, E. Hüllermeier, S. Glinca, G. Klebe, T. Pahikkala, A. Airola, B. De Baets, W. Wageman, IEEE/ACM Trans. Comput. Biology Bioinform. 11 (2014) 1157–1169.

Semi-Automated Software Composition Through Generated Components
F. Mohr, H. Kleine Büning, in: Proceedings of the 15th International Conference on Information Integration and Web-Based Applications &; Services (IiWAS), 2013, pp. 676–680.

Reducing Nondeterminism in Automated Service Composition
F. Mohr, T. Lettmann, H. Kleine Büning, in: Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 2013, pp. 154–161.

Labelwise versus pairwise decomposition in label ranking
W. Cheng, S. Henzgen, E. Hüllermeier, in: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany, 2013, pp. 129–136.

Rectifying classifier chains for multi-label classification, Bamberg, Germany
R. Senge, J. del Coz, E. Hüllermeier, in: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany, 2013, pp. 151–158.

Preference-based evolutionary optimization using generalized racing algorithms
R. Busa-Fekete, T. Fober, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier (Eds.), In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2013, pp. 237–246.

Weighted rank correlation measures based on fuzzy order relations
S. Henzgen, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier (Eds.), In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2013, pp. 227–236.

Interactive Q-learning with ordinal rewards and unreliable tutor
P. Weng, R. Busa-Fekete, E. Hüllermeier, in: In Proceedings ECML/PKDD-Workshop on Reinforcement Learning from Generalized Feedback:Beyond Numerical Rewards, Prague, 2013.

Preference-based evolutionary direct policy search
R. Busa-Fekete, B. Szörenyi, P. Weng, E. Hüllermeier, in: In Proceedings ECML/PKDD-Workshop on Reinforcement Learning from Generalized Feedback:Beyond Numerical Rewards, Prague, 2013.

A nearest neigbor approach to label ranking based on generalized labelwise loss minimization
W. Cheng, E. Hüllermeier, in: In Proceedings M-PREF, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China, 2013.

Event history analysis on data streams: An application to earthquake occurence
A. Shaker, E. Hüllermeier, in: G. Krempl, I. Zliobaite, Y. Wang, G. Forman (Eds.), In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic, 2013, pp. 38–41.

On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery
R. Senge, J.J. del Coz, E. Hüllermeier, in: L. Schmidt-Thieme, M. Spiliopoulou (Eds.), In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany , Springer, 2013.

Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics, Algorithms from and for Nature and Life
T. Fober, G. Klebe, E. Hüllermeier, in: B. Lausen, D. Van den Poel, A. Ultsch (Eds.), In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany, Springer, 2013, pp. 279–286.

Ordinal Choquistic regression 
A. Fallah Tehrani, E. Hüllermeier, in: J. Montero, G. Pasi, D. Ciucci (Eds.), In Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy, Atlantis Press, 2013.

Fuzzy Pattern Trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of colour yield in poyester dyeing, Edmonton, Canada
N. Nasiri, T. Fober, R. Senge, E. Hüllermeier, in: In Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada, 2013, pp. 715–721.

Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction
D. Heider, R. Senge, W. Cheng, E. Hüllermeier, Bioinformatics 29 (2013) 1946–1952.

Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. 
S. Bösner, K. Bönisch, J. Haasenritter , P. Schlegel, E. Hüllermeier, N. Donner-Banzhoff, BMC Family Practice 14 (2013) 154–162.

Diagnose im Kontext - eine erweiterte Perspektive
J. Haasenritter, A. Viniol, A. Becker, S. Bösner, E. Hüllermeier, R. Senge, N. Donner-Banzhoff, Zeitschrift Für Evidenz, Fortbildung Und Qualität Im Gesundheitswesen (ZEFQ) 107 (2013) 585–591.

Evolving fuzzy pattern trees for binary classification on data streams
A. Shaker, R. Senge, E. Hüllermeier, Information Sciences 220 (2013) 34–45.

Learning to rank lexical substitutions
G. Szarvas, R. Busa-Fekete, E. Hüllermeier, in: In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA, 2013.

Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization
K. Dembczynski, A. Jachnik, W. Kotlowski, W. Waegeman, E. Hüllermeier, in: S. Dasgupta, D. McAllester (Eds.), In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, 2013, pp. 1130–1138.

Top-k selection based on adaptive sampling of noisy preferences
R. Busa-Fekete, B. Szoreny, P. Weng, W. Cheng, E. Hüllermeier, in: S. Dasgupta, D. McAllester (Eds.), In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, 2013, pp. 1094–1102.

Preference-based CBR: General ideas and basic principles
E. Hüllermeier, W. Cheng, in: F. Rossi (Ed.), In Proceedings IJCAI-13, 23rd International Joint Conference on Artificial Intelligence, Beijing, China, AAAI Press, 2013, pp. 3012–3016.

Rule chains for visualizing evolving fuzzy rule-based systems
S. Henzgen, M. Strickert, E. Hüllermeier, in: R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, A. Zolnierek (Eds.), In Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, Springer, 2013, pp. 279–288.

Recovery analysis for adaptive learning from non-stationary data streams
A. Shaker, E. Hüllermeier, in: R. Burduk, K. Jackowski, M. Kurzynski, W. Wozniak, A. Zolnierek (Eds.), In Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, Springer, 2013, pp. 289–298.

GPU-based cloud computing for comparing the structure of protein binding sites
M. Leinweber, L. Baumgärtner, M. Mernberger, T. Fober, E. Hüllermeier, G. Klebe, B. Freisleben, in: In Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione D`Italia, Italy, 2012.

Efficient learning of classifiers based on the 2-additive Choquet integral
E. Hüllermeier, A. Fallah Tehrani, in: C. Moewes, A. Nürnberger (Eds.), Computational Intelligence in Intelligent Data Analysis, Springer, 2012, pp. 17–30.

Learning conditional lexicographic preference trees
M. Bräuning, E. Hüllermeier, in: In Workshops on Preference Learning at ECAI, European Conference on Artiticial Intelligence, Montpellier, France, 2012.

On the VC dimension of the Choquet integral
E. Hüllermeier, A. Fallah Tehrani, in: In Proceedings IPMU-2012 14th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Catania, Italy, 2012.

Learning  monotone nonlinear models using the Choquet integral
A. Fallah Tehrani, W. Cheng, K. Dembczynski, E. Hüllermeier, Machine Learning 89 (2012) 183–211.

Grouping, overlap and generalized bientropic functions for fuzzy modeling of pairwise comparisons
H. Bustince, M. Pagola, R. Mesiar, E. Hüllermeier, F. Herrera, IEEE Transactions on Fuzzy Systems 20 (2012) 405–415.

Preference-based reinforcement learning: A formal framework and a policy iteration algorithm
J. Fürnkranz, E. Hüllermeier, W. Cheng, S.H. Park, Machine Learning 89 (2012) 123–156.

On label dependence and loss  minimization in multi-label classification
K. Dembczynski, W. Waegeman, W. Cheng, E. Hüllermeier, Machine Learning 88 (2012) 5–45.

IBL Streams: A system for instance-based classification and regression on data streams
A. Shaker, E. Hüllermeier, Evolving Systems 3 (2012) 235–249.

A formal and empirical analysis of the fuzzy gamma rank correlation coefficient
M. Dolorez Ruiz, E. Hüllermeier, Information Sciences 206 (2012) 1–17.

Preference learning using the Choquet integral: The case of multipartite ranking
A. Fallah Tehrani, W. Cheng, E. Hüllermeier, IEEE Transactions on Fuzzy Systems 20 (2012) 1102–1113.

Fingerprint kernels for protein structure comparison
T. Fober, M. Mernberger, G. Klebe, E. Hüllermeier, Molecular Informatics 31 (2012) 443–452.

Comparing fuzzy partitions: A generalization of the Rand index and related measures
E. Hüllermeier, M. Rifqi, S. Henzgen, R. Senge, IEEE Transactions on Fuzzy Systems 20 (2012) 546–556.

Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung
R. Senge, T. Fober, N. Nasiri, E. Hüllermeier, At-Atomatisierungstechnik 60 (2012) 622–629.

Label ranking with partial abstention based on thresholded probalistic models
W. Cheng, E. Hüllermeier, W. Waegeman, V. Welker, in: In Proceedings NIPS-2012, 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012.

Probability estimation for mulit-class classification based on label ranking
W. Cheng, E. Hüllermeier, in: Proceedings ECML/PKDD-2012, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, UK, 2012.

Consistent multilabel ranking through univariate loss minimization
K. Dembczynski, W. Kotlowski, E. Hüllermeier, in: J. Langford, J. Pineau (Eds.), In Proceedings ICML-2012,  International Conference on Machine Learning, Edinburgh, Scotland, 2012.

An analysis of chaining in multi-label classification
K. Dembczynski, W. Waegeman, E. Hüllermeier, in: In Proceedings ECAI-2012, 20th European Conference on Artificial Inteligence, Montpellier, France , IOS Press, 2012, pp. 294–299.

Fuzzy rules in data mining: From fuzzy associations to gradual dependencies
E. Hüllermeier, in: E. Trillas, P.P. Bonissone, L. Magdalena, J. Kacprzyk (Eds.), Combining Experimentation and Theory, Springer, 2012, pp. 123–135.

Joint mode estimation in multi-label classification by chaining
K. Dembczynski, W. Waegeman, E. Hüllermeier, in: In Proceedings ECML Workshop on Collective Inference and Learning on Structured Data, Athens, Greece, 2011.

Prediction of protein localization for specialized compartments using time series kernels
M. Mernberger, D. Moog, S. Stork, S. Zauner, U. Maier, E. Hüllermeier, in: In Proceedings GCB-2011 German Conference on Bioinformatics Munich, Germany, 2011.

Comparing methods for knowledge-driven and data-driven fuzzy modeling: A case study in textile industry
M. Nasiri, E. Hüllermeier, R. Senge, E. Lughofer, in: In Proceedings IFSA-2011 World Congress of the International Fuzzy Systems Association, Surabaya and Bali Island, Indonesia, 2011, p. RW-103-1-6.

On-line redundancy deletion in evolving fuzzy regression models using a fuzzy inclusion measure
E. Lughofer, E. Hüllermeier, in: S. Galichet, J. Montero, G. Mauris (Eds.), In Proceedings EUSFLAT-2011 7th International Conference on the European Society for Fuzzy Logic and Technology, Aix-Les-Bains, France, 2011, pp. 380–387.

Choquistic regression: Generalizing logistic regression using the Choquet integral
A. Fallah Tehrani, W. Cheng, E. Hüllermeier, in: In Proceedings EUSFLAT-2011 7th International Conference on the European Society for Fuzzy Logic and Technology, Aix-Les-Bains, France, 2011, pp. 868–875.

Multivariate modeling to identify patterns in clinical data: The example of chest pain
O. Hirsch, S. Bösner, E. Hüllermeier, R. Senge, K. Dembczynski, N. Donner-Banzhoff, BMC Medical Research Methodology 11 (2011).

Preferences in AL: An overview
C. Domshlak, E. Hüllermeier, S. Kaci, H. Prade, Artificial Intelligence 175 (2011) 1037–1052.

Fuzzy machine learning and data mining
E. Hüllermeier, WIREs Data Mining and Knowledge Discovery 1 (2011) 269–283.

SEGA: Semi-global graph alignment for structure-based protein comparison
M. Mernberger, G. Klebe, E. Hüllermeier, IEEE/ACM Transactions of Computational Biology and Bioinformatics 8 (2011) 1330–1343.

Superposition and alignment of labeled point clouds
T. Fober, S. Glinca, G. Klebe, E. Hüllermeier, IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 (2011) 1653–1666.

Fuzzy sets in machine learning and data mining
E. Hüllermeier, Applied Soft Computing Journal (2011) 1493–1505.

Top-down induction of fuzzy pattern trees
R. Senge, E. Hüllermeier, IEEE Transactions on Fuzzy Systems 19 (2011) 241–252.

An exact algorithm for F-measure maximization
K. Dembczynski, W. Waegeman, W. Cheng, E. Hüllermeier, in: In Proceedings NIPS-2011, 25th Annual Conference on Neural Information Processing Systems, Granada, Spain, 2011.

Learning from label preferences
J. Fürnkranz, E. Hüllermeier, in: T. Elomaa, J. Hollmen, H. Mannila (Eds.), In Proceedings DS-2011, 14th International  Conference on Discovery Science, Number 6926 in LNAI, Springer, 2011, pp. 2–17.

Learning  monotone nonlinear models using the Choquet integral
A. Fallah Tehrani, W. Cheng, K. Dembczynski, E. Hüllermeier, in: In Proceedings ECML/PKDD-2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece, 2011.

Preference-based CBR: First steps toward a methodological framework
E. Hüllermeier, P. Schlegel, in: A. Ram, N. Wiratunga (Eds.), In Proceedings ICCBR-2011, 19th International Conference on Case-Based Reasoning, Number 6880 in LNAI, Springer, 2011, pp. 77–91.

Bipartite ranking through minimization of univariate loss
W. Kotlowski, K. Dembczynski, E. Hüllermeier, in: In Proceedings ICML-2011, 28th International Conference on Machine Learning, Washington, USA, 2011.

An Analysis of the FURIA algorithm for fuzzy rule induction
E. Hüllermeier, J. Hühn, in: J. Koronacki, Z.W. Ras, S.T. Wierzchon, J. Kacprzyk (Eds.), Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S.Michalski, Springer, 2010, pp. 321–344.

Preference Learning: An Introduction
J. Fürnkranz, E. Hüllermeier, in: J. Fürnkranz, E. Hüllermeier (Eds.), Preference Learning, Springer, 2010, pp. 1–18.

Efficient similarity retrieval for protein binding sites based on histogram comparison
T. Fober, M. Mernberger, G. Klebe, E. Hüllermeier, in: In Proceedings GCB-2010 German Conference on Bioinformatics Braunschweig, Germany, 2010, pp. 51–60.

On label dependence in multi-label classification
K. Dembczynski, W. Waegeman, W. Cheng, E. Hüllermeier, in: In Proceedings MLD-2010, 2nd International Workshop on Learning from Multi-Label Data, Haifa, Israel, 2010.

Similarity measures for protein structures based on fuzzy histogram comparison
T. Fober, E. Hüllermeier, in: In Proceedings WCCI-2010, World Congress on Computational Intelligence, Barcelona, Spain, 2010.

Pattern trees for regression and fuzzy systems modeling
R. Senge, E. Hüllermeier, in: In Proceedings WCCI-2010, World Congress on Computational Intelligence, Barcelona, Spain, 2010.

GARLig: A fully automated tool for subset selection of large fragment spaces via a self-adaptive genetic algorithm
P. Pfeffer, T. Fober, E. Hüllermeier, G. Klebe, Journal of Chemical Information and Modeling 50 (2010) 1644–1659.

Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting
E. Hüllermeier, S. Vanderlooy, Pattern Recognition 43 (2010) 128–142.

On predictive accuracy and risk minimization in pairwise label ranking
E. Hüllermeier, J. Fürnkranz, Journal of Computer and System Sciences 76 (2010) 49–62.

Bayes optimal multilabel classification via probalistic classifier chains
K. Dembczynski, W. Cheng, E. Hüllermeier, in: J. Fürnkranz, T. Joachims (Eds.), In Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel, 2010, pp. 279–286.

Label ranking based on the Plackett-Luce model
W. Cheng, K. Dembczynski, E. Hüllermeier, in: J. Fürnkranz, T. Joachims (Eds.), In Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel, 2010, pp. 215–222.

Graded multi-label classification: The ordinal case
W. Cheng, K. Dembczynski, E. Hüllermeier, in: J. Fürnkranz, T. Joachims (Eds.), In Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel, 2010.

Regret analysis for performance metrics in multi-label classification: The case of Hamming and subset zero-one loss
K. Dembczynski, W. Waegeman, W. Cheng, E. Hüllermeier, in: In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain, 2010.

Predicting partial orders: Ranking with abstention
W. Cheng, M. Rademaker, B. De Beats, E. Hüllermeier, in: In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain, 2010.

Uncertainty in clustering and classification 
E. Hüllermeier, in: In Proceedings SUM 2010, International Conference on Scalable Uncertainty Management , Springer, 2010, pp. 16–19.

A simple instance-based approach to multilabel classification using the Mallows model
W. Cheng, E. Hüllermeier, in: In Proceedings MLD-2009 1st  International Workshop on Learning from Multi-Label Data, Bled, Slovenia, 2009, pp. 28–38.

Learning pattern tree classifiers using a co-evolutionary algorithm
R. Senge, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier (Eds.), In Proceedings 19th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2009, pp. 22–33.

Graph-kernels for the comparative analysis of protein active sites
T. Fober, M. Mernberger, R. Moritz, E. Hüllermeier, in: I. Grosse, S. Neumann, S. Posch, F. Schreiber, P.. Stadler (Eds.), In Proceedings GCB-2009 German Conference on Bioinformatics Halle (Saale), Germany, 2009, pp. 21–31.

Learning pattern tree classifiers using a co-evolutionary algorithm
R. Senge, E. Hüllermeier, in: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Darmstadt, Germany, 2009, pp. 105–110.

Extension and empirical comparison of graph-kernels for the analysis of protein active sites
T. Fober, M. Mernberger, V. Melnikov, R. Moritz, E. Hüllermeier, in: In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Darmstadt, Germany, 2009, pp. 30–36.

Efficient construction of multiple geometrical alignments for the comparison of protein binding sites
T. Fober, G. Klebe, E. Hüllermeier, in: In Proceedings ISDA-2009, 9th International Conference on Intelligent Systems Design and Applications, Pisa, Italy, 2009, pp. 1251–1256.

Similarity analysis of protein binding sites: A generalization of the maximum common subgraph measure based on quasi-clique detection
I. Boukhris, Z. Elouedi, T. Fober, M. Mernberger, E. Hüllermeier, in: In Proceedings IDA-2009, 9th International Symposium on Intelligent Data Analysis, Pisa / Italy, 2009, pp. 1245–1250.

Fuzzy modeling of labeled point cloud superposition  for the comparison of protein binding sites
T. Fober, E. Hüllermeier, in: In Proceedings IFSA / EUSFLAT-2009 World Congress of  the Fuzzy Systems Association, Lissabon, Portugal, 2009, pp. 1299–1304.

A fuzzy variant of the Rand index for comparing clustering structures
E. Hüllermeier, M. Rifqi, in: In Proceedings IFSA / EUSFLAT-2009 World Congress of  the Fuzzy Systems Association, Lissabon, Portugal, 2009, pp. 1294–1298.

Merging chemical and biological space: Structural mapping of enzyme binding pocket space
N. Weskamp, E. Hüllermeier, G. Klebe, Proteins 76 (2009) 317–330.