Jasinska, K., Dembczynski, K., Busa-Fekete, R., Klerx, T., &; Hüllermeier, E. (2016). Extreme F-measure maximization using sparse probability estimates . In M. F. Balcan &; K. Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA.

Melnikov, V., &; Hüllermeier, E. (2016). Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016. In European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 756–771).

Dembczynski, K., Kotlowski, W., Waegeman, W., Busa-Fekete, R., &; Hüllermeier, E. (2016). Consistency of probalistic classifier trees. In In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 511–526).

Shabani, A., Paul, A., Platon, R., &; Hüllermeier, E. (2016). Predicting the electricity consumption of buildings: An improved CBR approach. In In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA (pp. 356–369).

Pfannschmidt, K., Hüllermeier, E., Held, S., &; Neiger, R. (2016). Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.

Labreuche, C., Hüllermeier, E., Vojtas, P., &; Fallah Tehrani, A. (2016). On the Identifiability of models in multi-criteria preference learning . In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.

Schäfer, D., &; Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.

Couso, I., Ahmadi Fahandar, M., &; Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.

Lu, S., &; Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy supersets losses. In F. Hoffmann, E. Hüllermeier, &; R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing (pp. 1–8).

Schäfer, D., &; Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In In Workshop LWDA “Lernen, Wissen, Daten, Analysen.”

Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F., &; van Harmelen, F. (Eds.). (2016). ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence (Vol. 285). The Hague, The Netherlands: IOS Press.

Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., &; Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.

Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., &; Heider, D. (2016). 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).

Arifulina, S., Platenius, M. C., Mohr, F., Engels, G., &; Schäfer, W. (2015). Market-Specific Service Compositions: Specification and Matching. In Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet (pp. 333--340).https://doi.org/10.1109/SERVICES.2015.58 

Jungmann, A., &; Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications, (1), 1–18.https://doi.org/10.1186/s13174-015-0022-8 

Mohr, F. (2015). A Metric for Functional Reusability of Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 298--313).https://doi.org/10.1007/978-3-319-14130-5_21 

Mohr, F., Jungmann, A., &; Kleine Büning, H. (2015). Automated Online Service Composition. In Proceedings of the 12th IEEE International Conference on Services Computing (SCC) (pp. 57--64).https://doi.org/10.1109/SCC.2015.18 

Senge, R., &; Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.https://doi.org/10.1109/tfuzz.2015.2396078 

Schäfer, D., &; Hüllermeier, E. (2015). Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal (pp. 110–111).

Paul, A., &; Hüllermeier, E. (2015). A cbr approach to the angry birds game. In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany (pp. 68–77).

Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., &; Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In F. Hoffmann &; E. Hüllermeier (Eds.), In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany (pp. 235–240). KIT Scientific Publishing.

Lu, S., &; Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In F. Hoffmann &; E. Hüllermeier (Eds.), in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany (pp. 97–104). KIT Scientific Publishing.

Senge, R., &; Hüllermeier, E. (2015). Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.

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

Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.

Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., &; Hüllermeier, E. (2015). On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research, 15, 3313–3368.

Shaker, A., &; Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.

Hüllermeier, E., &; Minor, M. (2015). Case-Based Reasoning Research and Development . In in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer.

Hoffmann, F., &; Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.

Abdel-Aziz, A., &; Hüllermeier, E. (2015). Case Base Maintenance in Preference-Based CBR. In In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 1–14).

Szörényi, B., Busa-Fekete, R., Weng, P., &; Hüllermeier, E. (2015). Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In In Proceedings International Conference on Machine Learning (ICML 2015) (pp. 1660–1668).

Schäfer, D., &; Hüllermeier, E. (2015). Dyad Ranking Using A Bilinear Plackett-Luce Model. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 227–242).

Hüllermeier, E., &; Cheng, W. (2015). Superset Learning Based on Generalized Loss Minimization . In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 260–275).

Henzgen, S., &; Hüllermeier, E. (2015). Weighted Rank Correlationf: A Flexible Approach Based on Fuzzy Order Relations. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 422–437).

Szörényi, B., Busa-Fekete, R., Paul, A., &; Hüllermeier, E. (2015). Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 604–612).

Szörényi, B., Busa-Fekete, R., Dembczynski, K., &; Hüllermeier, E. (2015). Online F-Measure Optimization. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 595–603).

El Mesaoudi-Paul, A., &; Hüllermeier, E. (2015). A CBR Approach to the Angry Birds Game. In in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 68–77).

Schäfer, D., &; Hüllermeier, E. (2015). Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations. In in Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML) (pp. 110–111).

Lu, S., &; Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In Proceedings 25. Workshop Computational Intelligence (pp. 97–104).

Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., &; Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In Proceedings 25. Workshop Computational Intelligence (pp. 235–240).

Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., &; Hüllermeier, E. (2015). On the Bayes-Optimality of F-Measure Maximizers. In Journal of Machine Learning Research, 15, 3333–3388.

Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.

Shaker, A., &; Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.

Hüllermeier, E. (2015). From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.

Garcia-Jimenez, S., Bustince, U., Hüllermeier, E., Mesiar, R., Pal, N. R., &; Pradera, A. (2015). Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 23(4), 1259–1273.

Senge, R., &; Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.

Basavaraju, M., Chandran, L. S., Rajendraprasad, D., &; Ramaswamy, A. (2014). Rainbow connection number of graph power and graph products. Graphs and Combinatorics, 30(6), 1363–1382.

Basavaraju, M., Chandran, L. S., Rajendraprasad, D., &; Ramaswamy, A. (2014). Rainbow connection number and radius. Graphs and Combinatorics, 30(2), 275–285.

Mohr, F., &; Walther, S. (2014). Template-based Generation of Semantic Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 188–203).https://doi.org/10.1007/978-3-319-14130-5_14 

Jungmann, A., Mohr, F., &; Kleinjohann, B. (2014). Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. In Proceedings of the 10th World Congress on Services (SERVICES) (pp. 346–353).https://doi.org/10.1109/SERVICES.2014.68 

Jungmann, A., Mohr, F., &; Kleinjohann, B. (2014). Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. In Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA) (pp. 105–112).https://doi.org/10.1109/SOCA.2014.48 

Mohr, F. (2014). Estimating Functional Reusability of Services. In Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC) (pp. 411–418).

Agarwal, M., Fallah Tehrani, A., &; Hüllermeier, E. (2014). Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis, 22(3–4).

Krotzky, T., Fober, T., Hüllermeier, E., &; Klebe, G. (2014). Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 11(5), 878–890.

Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning, 55(7), 1519–1534.

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

Busa-Fekete, R., Szörenyi, 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.

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.

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

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

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.

Donner-Banzhoff, N., Haasenritter, J., Hüllermeier, E., Viniol, A., Bösner, S., &; Becker, A. (2014). The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology, 2(67), 124–132.

Busa-Fekete, R., Szörényi, B., &; Hüllermeier, E. (2014). PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In Proceedings AAAI 2014, Quebec, Canada (pp. 1701–1707).

Busa-Fekete, R., &; Hüllermeier, E. (2014). A Survey of Preference-Based Online Learning with Bandit Algorithms. In Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia (pp. 18–39).

Henzgen, S., &; Hüllermeier, E. (2014). Mining Rank Data. In Proceedings Discovery Science, Bled,Slovenia  (pp. 123–134).

Fallah Tehrani, A., Strickert, M., &; Hüllermeier, E. (2014). The Choquet kernel for monotone data. In Proceedings ESANN , Bruges, Belgium.

Abdel-Aziz, A., Strickert, M., &; Hüllermeier, E. (2014). Learning Solution Similarity in Preference-Based CBR. In Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland (pp. 17–31).

Schäfer, D., &; Hüllermeier, E. (2014). Dyad Ranking Using A Bilinear Plackett-Luce Model. In Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany (pp. 32–33).

Calders, T., Esposito, F., Hüllermeier, E., &; Meo, R. (2014). Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In Proceedings, Parts I-III. Lecture Notes in Computer Science (pp. 8724–8726). Springer.

Fürnkranz, J., Hüllermeier, E., Rudin, C., Slowinski, R., &; Sanner, S. (2014). Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports (Vol. 4, pp. 1–27).

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.