| Bayes optimal multilabel classification via probabilistic classifier chains K Dembczyński, W Cheng, E Hüllermeier Proceedings of the 27th international conference on machine learning (ICML …, 2010 | 644 | 2010 |
| On label dependence and loss minimization in multi-label classification K Dembczyński, W Waegeman, W Cheng, E Hüllermeier Machine Learning 88 (1-2), 5-45, 2012 | 471 | 2012 |
| 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 | 208 | 2014 |
| Rough set approach to multiple criteria classification with imprecise evaluations and assignments K Dembczyński, S Greco, R Słowiński European Journal of Operational Research 198 (2), 626-636, 2009 | 175 | 2009 |
| Stochastic dominance-based rough set model for ordinal classification W Kotłowski, K Dembczyński, S Greco, R Słowiński Information Sciences 178 (21), 4019-4037, 2008 | 172 | 2008 |
| An exact algorithm for F-measure maximization K Dembczynski, W Waegeman, W Cheng, E Hüllermeier Advances in neural information processing systems 24, 2011 | 154 | 2011 |
| Learning monotone nonlinear models using the Choquet integral A Fallah Tehrani, W Cheng, K Dembczyński, E Hüllermeier Machine Learning 89, 183-211, 2012 | 153 | 2012 |
| 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 International conference on machine learning, 1130-1138, 2013 | 135 | 2013 |
| Label ranking methods based on the Plackett-Luce model W Cheng, E Hüllermeier, KJ Dembczynski Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 131 | 2010 |
| Extreme F-measure maximization using sparse probability estimates K Jasinska, K Dembczyński, R Busa-Fekete, K Pfannschmidt, T Klerx, ... International Conference on Machine Learning, 1435-1444, 2016 | 127 | 2016 |
| Multi-target prediction: a unifying view on problems and methods W Waegeman, K Dembczyński, E Hüllermeier Data Mining and Knowledge Discovery 33, 293-324, 2019 | 111 | 2019 |
| Bipartite ranking through minimization of univariate loss W Kotlowski, KJ Dembczynski, E Huellermeier Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 109 | 2011 |
| A no-regret generalization of hierarchical softmax to extreme multi-label classification M Wydmuch, K Jasinska, M Kuznetsov, R Busa-Fekete, K Dembczyński Advances in Neural Information Processing Systems, 6358-6368, 2018 | 107 | 2018 |
| Predicting ads click-through rate with decision rules K Dembczynski, W Kotlowski, D Weiss Workshop on targeting and ranking in online advertising 2008, 2008 | 93 | 2008 |
| ENDER: a statistical framework for boosting decision rules K Dembczyński, W Kotłowski, R Słowiński Data Mining and Knowledge Discovery 21, 52-90, 2010 | 90 | 2010 |
| On the Bayes-optimality of F-measure maximizers W Waegeman, K Dembczyński, A Jachnik, W Cheng, E Hüllermeier The Journal of Machine Learning Research 15 (1), 3333-3388, 2014 | 84 | 2014 |
| An Analysis of Chaining in Multi-Label Classification. K Dembczyński, W Waegeman, E Hüllermeier ECAI, 294-299, 2012 | 82 | 2012 |
| Regret analysis for performance metrics in multi-label classification: the case of hamming and subset zero-one loss K Dembczyński, W Waegeman, W Cheng, E Hüllermeier Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010 | 77 | 2010 |
| Generation of exhaustive set of rules within dominance-based rough set approach K Dembczyński, R Pindur, R Susmaga Electronic Notes in Theoretical Computer Science 82 (4), 96-107, 2003 | 72 | 2003 |
| Graded multilabel classification: The ordinal case W Cheng, E Hüllermeier, KJ Dembczynski Proceedings of the 27th international conference on machine learning (ICML …, 2010 | 69 | 2010 |
Eyke HüllermeierProfessor of Computer Science, Paderborn UniversityVerified email at upb.de
