Krzysztof Dembczyński
Krzysztof Dembczyński
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Geciteerd door
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 Dembczynski, W Waegeman, W Cheng, E Hüllermeier
Advances in neural information processing systems 24, 2011
Learning monotone nonlinear models using the Choquet integral
A Fallah Tehrani, W Cheng, K Dembczyński, E Hüllermeier
Machine Learning 89 (1), 183-211, 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
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
Label ranking methods based on the Plackett-Luce model
W Cheng, K Dembczynski, E Hüllermeier
ICML, 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
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, KJ Dembczynski, E Huellermeier
Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011
Predicting ads clickthrough rate with decision rules
K Dembczynski, W Kotlowski, D Weiss
Workshop on targeting and ranking in online advertising 2008, 2008
Ender: a statistical framework for boosting decision rules
K Dembczyński, W Kotłowski, R Słowiński
Data Mining and Knowledge Discovery 21 (1), 52-90, 2010
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
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
Multi-target prediction: a unifying view on problems and methods
W Waegeman, K Dembczyński, E Hüllermeier
Data Mining and Knowledge Discovery 33 (2), 293-324, 2019
Maximum likelihood rule ensembles
K Dembczyński, W Kotłowski, R Słowiński
Proceedings of the 25th international conference on Machine learning, 224-231, 2008
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