%0 Journal Article
%T A game-theoretic framework for classifier ensembles using weighted majority voting with local accuracy estimates
%A Harris V. Georgiou
%A Michael E. Mavroforakis
%J Computer Science
%D 2013
%I arXiv
%X In this paper, a novel approach for the optimal combination of binary classifiers is proposed. The classifier combination problem is approached from a Game Theory perspective. The proposed framework of adapted weighted majority rules (WMR) is tested against common rank-based, Bayesian and simple majority models, as well as two soft-output averaging rules. Experiments with ensembles of Support Vector Machines (SVM), Ordinary Binary Tree Classifiers (OBTC) and weighted k-nearest-neighbor (w/k-NN) models on benchmark datasets indicate that this new adaptive WMR model, employing local accuracy estimators and the analytically computed optimal weights outperform all the other simple combination rules.
%U http://arxiv.org/abs/1302.0540v1