Shaker, A., &; Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Applied Mathematics and Computer Science, 24(1), 199–212.

Hoffmann, F., Hüllermeier, E., &; Kroll, A. (2014). Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik, 62(10), 685–686.

Calders, T., Esposito, F., Hüllermeier, E., &; Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Data Min. Knowledge Discovery, 28(5–6), 1129–1133.

Henzgen, S., Strickert, M., &; Hüllermeier, E. (2014). Visualization of evolving fuzzy rule-based systems. Evolving Systems, 5(3), 175–191.

Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1519–1534.

Hüllermeier, E. (2014). Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1609–1613.

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

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

Mernberger, M., Moog, M., Stork, S., Zauner, S., Maier, U. G., &; Hüllermeier, E. (2014). Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J. Bioinformatics and Computational Biology, 12(1).

Calders, T., Esposito, F., Hüllermeier, E., &; Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Machine Learning, 97(1–2), 1–3.

Busa-Fekete, R., Szörényi, B., Weng, P., Cheng, W., &; Hüllermeier, E. (2014). Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.

Montanés, E., Senge, R., Barranquero, J., Quevedo, J. R., Del Coz, J. J., &; Hüllermeier, E. (2014). Dependent binary relevance models for multi-label classification. Pattern Recognition, 47(3), 1494–1508.

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

Krotzky, T., Fober, T., Hüllermeier, E., &; Klebe, G. (2014). Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Trans. Comput. Biology Bioinform., 11(5), 878–890.

Stock, M., Fober, T., Hüllermeier, E., Glinca, S., Klebe, G., Pahikkala, T.,  Wageman, W. (2014). Identification of Functionally Releated Enzymes by Learning to Rank Methods. IEEE/ACM Trans. Comput. Biology Bioinform., 11(6), 1157–1169.

Mohr, F., &; Kleine Büning, H. (2013). Semi-Automated Software Composition Through Generated Components. In Proceedings of the 15th International Conference on Information Integration and Web-based Applications &; Services (iiWAS) (pp. 676–680).https://doi.org/10.1145/2539150.2539235 

Mohr, F., Lettmann, T., &; Kleine Büning, H. (2013). Reducing Nondeterminism in Automated Service Composition. In Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA) (pp. 154–161).https://doi.org/10.1109/SOCA.2013.25 

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

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

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

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

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

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

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

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

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

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

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

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

Heider, D., Senge, R., Cheng, W., &; Hüllermeier, E. (2013). Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistence prediction. Bioinformatics, 29(16), 1946–1952.

Bösner, S., Bönisch, K., Haasenritter , J., Schlegel, P., Hüllermeier, E., &; Donner-Banzhoff, N. (2013). Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. . BMC Family Practice, 14(1), 154–162.

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

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

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

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

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

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

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

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

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

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

Bräuning, M., &; Hüllermeier, E. (2012). Learning conditional lexicographic preference trees. In In Workshops on Preference Learning at ECAI, European Conference on Artiticial intelligence, Montpellier, France.

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

Fallah Tehrani, A., Cheng, W., Dembczynski, K., &; Hüllermeier, E. (2012). Learning  monotone nonlinear models using the Choquet integral. Machine Learning, 89(1), 183–211.

Bustince, H., Pagola, M., Mesiar, R., Hüllermeier, E., &; Herrera, F. (2012). Grouping, overlap and generalized bientropic functions for fuzzy modeling of pairwise comparisons. IEEE Transactions on Fuzzy Systems, 20(3), 405–415.

Fürnkranz, J., Hüllermeier, E., Cheng, W., &; Park, S. H. (2012). Preference-based reinforcement learning: A formal framework and a policy iteration algorithm. Machine Learning, 89(1), 123–156.

Dembczynski, K., Waegeman, W., Cheng, W., &; Hüllermeier, E. (2012). On label dependence and loss  minimization in multi-label classification. Machine Learning, 88(1–2), 5–45.

Shaker, A., &; Hüllermeier, E. (2012). IBL Streams: A system for instance-based classification and regression on data streams. Evolving Systems, 3(4), 235–249.

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

Fallah Tehrani, A., Cheng, W., &; Hüllermeier, E. (2012). Preference learning using the Choquet integral: The case of multipartite ranking. IEEE Transactions on Fuzzy Systems, 20(6), 1102–1113.

Fober, T., Mernberger, M., Klebe, G., &; Hüllermeier, E. (2012). Fingerprint kernels for protein structure comparison. Molecular Informatics, 31(6–7), 443–452.

Hüllermeier, E., Rifqi, M., Henzgen, S., &; Senge, R. (2012). Comparing fuzzy partitions: A generalization of the Rand index and related measures. IEEE Transactions on Fuzzy Systems, 20(3), 546–556.

Senge, R., Fober, T., Nasiri, N., &; Hüllermeier, E. (2012). Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung. At-Atomatisierungstechnik, 60(10), 622–629.

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

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

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

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

Hüllermeier, E. (2012). Fuzzy rules in data mining: From fuzzy associations to gradual dependencies. In E. Trillas, P. P. Bonissone, L. Magdalena, &; J. Kacprzyk (Eds.), Combining Experimentation and Theory (Vol. 271, pp. 123–135). Springer.

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

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

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

Lughofer, E., &; Hüllermeier, E. (2011). On-line redundancy deletion in evolving fuzzy regression models using a fuzzy inclusion measure. 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 (pp. 380–387).

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

Hirsch, O., Bösner, S., Hüllermeier, E., Senge, R., Dembczynski, K., &; Donner-Banzhoff, N. (2011). Multivariate modeling to identify patterns in clinical data: The example of chest pain. BMC Medical Research Methodology, 11(155).

Domshlak, C., Hüllermeier, E., Kaci, S., &; Prade, H. (2011). Preferences in AL: An overview. Artificial Intelligence, 175(7–8), 1037–1052.

Hüllermeier, E. (2011). Fuzzy machine learning and data mining. WIREs Data Mining and Knowledge Discovery, 1(4), 269–283.

Mernberger, M., Klebe, G., &; Hüllermeier, E. (2011). SEGA: Semi-global graph alignment for structure-based protein comparison. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 8(5), 1330–1343.

Fober, T., Glinca, S., Klebe, G., &; Hüllermeier, E. (2011). Superposition and alignment of labeled point clouds. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6), 1653–1666.

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

Senge, R., &; Hüllermeier, E. (2011). Top-down induction of fuzzy pattern trees. IEEE Transactions on Fuzzy Systems, 19(2), 241–252.

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

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

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

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

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

Hüllermeier, E., &; Hühn, J. (2010). An Analysis of the FURIA algorithm for fuzzy rule induction. 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 (Vol. 262, pp. 321–344). Springer.

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

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

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

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

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

Pfeffer, P., Fober, T., Hüllermeier, E., &; Klebe, G. (2010). GARLig: A fully automated tool for subset selection of large fragment spaces via a self-adaptive genetic algorithm. Journal of Chemical Information and Modeling, 50(9), 1644–1659.

Hüllermeier, E., &; Vanderlooy, S. (2010). Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognition, 43(1), 128–142.

Hüllermeier, E., &; Fürnkranz, J. (2010). On predictive accuracy and risk minimization in pairwise label ranking. Journal of Computer and System Sciences, 76(1), 49–62.

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

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

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

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

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

Hüllermeier, E. (2010). Uncertainty in clustering and classification . In in Proceedings SUM 2010, International Conference on Scalable Uncertainty Management  (Vol. 6379, pp. 16–19). Springer.

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

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

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

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

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

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

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

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

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

Weskamp, N., Hüllermeier, E., &; Klebe, G. (2009). Merging chemical and biological space: Structural mapping of enzyme binding pocket space. Proteins, 76(2), 317–330.