Global reweighting and weight vector based strategy for multiclass boosting

Abstract

Boosting is a generic statistical process for generating accurate classifier ensembles from moderately accurate learning algorithm. This paper presents a new generic boosting style procedure, M-Boost, for learning multiclass concepts. M-Boost uses a global strategy for selecting the weak classifier, a global weight reassignment strategy, a vector valued weight for the selected classifiers, and an ensemble that outputs a probability distribution on classes. © 2012 Springer-Verlag.