Cost-sensitive learning based on Bregman divergences

Authors:
Raúl Santos-Rodríguez;Alicia Guerrero-Curieses;Rocío Alaiz-Rodríguez;Jesús Cid-Sueiro
Affiliations:
Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés (Madrid), Spain;Department of Signal Theory and Communications, Universidad Rey Juan Carlos, Fuenlabrada (Madrid), Spain;Department of Electrical and Electronic Engineering, Universidad de León, León, Spain;Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés (Madrid), Spain
Venue:
Machine Learning
Year:
2009

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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.