PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Cost Sensitive Learning based on Bregman Divergences
Raúl Santos-Rodríguez, Alicia Guerrero-Curieses, Rocío Alaiz-Rodríguez and Jesús Cid-Sueiro
Machine Learning Volume 76, Number 2-3, pp. 271-285, 2009. ISSN 1573-0565

Abstract

This paper analyzes the application of a particular class of Bregman divergences to design cost-sensitive classifiers for multiclass problems. We show that these divergence measures can be used to estimate posterior probabilities with maximal accuracy for the probability values that are close to the decision boundaries. Asymptotically, the proposed divergence measures provide classifiers minimizing the sum of decision costs in non-separable problems, and maximizing a margin in separable MAP problems.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6456
Deposited By:Jesus Cid-Sueiro
Deposited On:08 March 2010