W
Wojciech Kotłowski
Researcher at Poznań University of Technology
Publications - 79
Citations - 1441
Wojciech Kotłowski is an academic researcher from Poznań University of Technology. The author has contributed to research in topics: Regret & Rough set. The author has an hindex of 19, co-authored 76 publications receiving 1294 citations. Previous affiliations of Wojciech Kotłowski include Centrum Wiskunde & Informatica.
Papers
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Journal ArticleDOI
Stochastic dominance-based rough set model for ordinal classification
TL;DR: A probabilistic model for ordinal classification problems with monotonicity constraints is introduced and the equivalence of the variable consistency rough sets to the specific empirical risk-minimizing decision rule in the statistical decision theory is shown.
Proceedings Article
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization
Krzysztof Dembczyński,Arkadiusz Jachnik,Wojciech Kotłowski,Willem Waegeman,Eyke Huellermeier +4 more
TL;DR: A novel plug-in rule algorithm is introduced that estimates all parameters required for a Bayes-optimal prediction via a set of multinomial regression models, and this algorithm is compared with SSVMs in terms of computational complexity and statistical consistency.
Proceedings Article
Bipartite Ranking through Minimization of Univariate Loss
Wojciech Kotłowski,Wojciech Kotłowski,Krzysztof Dembczyński,Krzysztof Dembczyński,Eyke H llermeier +4 more
TL;DR: In this article, the authors show that the real gain is obtained through margin-based loss functions, for which they are able to derive proper bounds, not only for rank risk but, more importantly, also for rank regret.
Journal ArticleDOI
ENDER: a statistical framework for boosting decision rules
TL;DR: A learning algorithm, called ENDER, which constructs an ensemble of decision rules, which is tailored for regression and binary classification problems and uses the boosting approach for learning, which can be treated as generalization of sequential covering.
Journal ArticleDOI
On Nonparametric Ordinal Classification with Monotonicity Constraints
TL;DR: This paper provides a statistical framework for classification with monotonicity constraints, and considers two approaches to classification in the nonparametric setting: the "plug-in" method (classification by estimating first the class conditional distribution) and the direct method ( classification by minimization of the empirical risk).