Krzysztof Dembczyński
Krzysztof Dembczyński
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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
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
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
Stochastic dominance-based rough set model for ordinal classification
W Kotłowski, K Dembczyński, S Greco, R Słowiński
Information Sciences 178 (21), 4019-4037, 2008
An exact algorithm for F-measure maximization
K Dembczyński, W Waegeman, W Cheng, E Hüllermeier
2011 Neural Information Processing Systems (NIPS 2011) 24, 2011
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
Learning monotone nonlinear models using the Choquet integral
AF Tehrani, W Cheng, K Dembczyński, E Hüllermeier
Machine Learning 89 (1), 183-211, 2012
Label ranking methods based on the Plackett-Luce model
W Cheng, K Dembczynski, E Hüllermeier
ICML, 2010
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
On label dependence in multilabel classification
K Dembczynski, W Waegeman, W Cheng, E Hüllermeier
ICML/COLT Workshop on Learning from Multi-label data, 2010
Bipartite ranking through minimization of univariate loss
W Kotlowski, K Dembczynski, E Huellermeier
ICML, 2011
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
Predicting ads clickthrough rate with decision rules
K Dembczynski, W Kotlowski, D Weiss
Workshop on targeting and ranking in online advertising 2008, 2008
An Analysis of Chaining in Multi-Label Classification.
K Dembczyński, W Waegeman, E Hüllermeier
ECAI, 294-299, 2012
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
Ender: a statistical framework for boosting decision rules
K Dembczyński, W Kotłowski, R Słowiński
Data Mining and Knowledge Discovery 21 (1), 52-90, 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
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
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
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2010
Dominance-based rough set classifier without induction of decision rules
K Dembczyński, R Pindur, R Susmaga
Electronic Notes in Theoretical Computer Science 82 (4), 84-95, 2003
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