| Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty R Senge, S Bösner, K Dembczyński, J Haasenritter, O Hirsch, ... Information Sciences 255, 16-29, 2014 | 207 | 2014 |
| Dependent binary relevance models for multi-label classification E Montanes, R Senge, J Barranquero, JR Quevedo, JJ del Coz, ... Pattern Recognition 47 (3), 1494-1508, 2014 | 173 | 2014 |
| Comparing fuzzy partitions: A generalization of the rand index and related measures E Hullermeier, M Rifqi, S Henzgen, R Senge IEEE Transactions on Fuzzy Systems 20 (3), 546-556, 2011 | 132 | 2011 |
| Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction D Heider, R Senge, W Cheng, E Hüllermeier Bioinformatics 29 (16), 1946-1952, 2013 | 90 | 2013 |
| Top-down induction of fuzzy pattern trees R Senge, E Hüllermeier IEEE Transactions on Fuzzy Systems 19 (2), 241-252, 2010 | 85 | 2010 |
| On the Problem of Error Propagation in Classifier Chains for Multi-Label Classification⋆ R Senge, JJ del Coz, E Hüllermeier Data Analysis, Machine Learning and Knowledge Discovery, 163-170, 2014 | 68 | 2014 |
| Distributional Regression for Demand Forecasting in e-grocery H Jahnke, M Ulrich, R Pesch, R Senge, R Langrock European Journal of Operational Research, 2019 | 54* | 2019 |
| Rectifying classifier chains for multi-label classification R Senge, JJ del Coz, E Hüllermeier Lernen, Wissen, Adaption 2013, 162-169, 2013 | 48* | 2013 |
| Evolving Fuzzy Pattern Trees for Binary Classification on Data Streams A Shaker, R Senge, E Hüllermeier Information Science, 34-45, 2013 | 45 | 2013 |
| Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification M Riemenschneider, R Senge, U Neumann, E Hüllermeier, D Heider BioData mining 9, 1-6, 2016 | 38 | 2016 |
| Classification-based model selection in retail demand forecasting M Ulrich, H Jahnke, R Langrock, R Pesch, R Senge International Journal of Forecasting 38 (1), 209-223, 2022 | 35 | 2022 |
| Fast fuzzy pattern tree learning for classification R Senge, E Huellermeier IEEE Transactions on Fuzzy Systems 23 (6), 2024-2033, 2015 | 27* | 2015 |
| Pattern trees for regression and fuzzy systems modeling R Senge, E Hüllermeier International Conference on Fuzzy Systems, 1-7, 2010 | 23 | 2010 |
| Multivariate modeling to identify patterns in clinical data: the example of chest pain O Hirsch, S Bösner, E Hüllermeier, R Senge, K Dembczynski, ... BMC medical research methodology 11, 1-10, 2011 | 15 | 2011 |
| José Barranquero, José Ramón Quevedo, Juan José del Coz, and Eyke Hüllermeier. 2014. Dependent binary relevance models for multi-label classification E Montañés, R Senge Pattern Recognition 47 (3), 1494-1508, 2014 | 10 | 2014 |
| Comparing methods for knowledge-driven and data-driven fuzzy modeling: A case study in textile industry M Nasiri, E Hüllermeier, R Senge, E Lughofer Proceedings IFSA–2011, World Congress of the International Fuzzy Systems …, 2011 | 9 | 2011 |
| Fuzzy Pattern Trees as an Alternative to Rule-based Fuzzy Systems: Knowledge-driven, Data-driven and Hybrid Modeling of Color Yield in Polyester Dyeing M Nasiri, T Fober, R Senge, E Hüllermeier IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint, 2013 | 7 | 2013 |
| Machine learning methods for fuzzy pattern tree induction R Senge Philipps-Universität Marburg, 2014 | 6 | 2014 |
| Accuracy of diagnostic tests for feline infectious peritonitis (FIP) in cats with body cavity effusion S Held, M König, HP Hamann, R Senge, E Hüllermeier, R Neiger Proceedings of the European College of Veterinary Internal Medicine Congress …, 2011 | 6 | 2011 |
| Diagnosis in context-broadening the perspective J Haasenritter, A Viniol, A Becker, S Bösner, E Hüllermeier, R Senge, ... Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen 107 (9 …, 2012 | 5* | 2012 |
Eyke HüllermeierProfessor of Computer Science, Paderborn UniversityVerified email at upb.de
