Journals Proceedings

Abstract

Pruning an ensemble of classifiers is one of the most significant and effective issues in ensemble method topic. This paper presents a new ensemble pruning method inspired by upward stochastic walking idea. Our proposed method incorporates simulated annealing algorithm and forward selection method for selecting models through the ensemble according to the probabilistic steps. Experimental comparisons of the proposed method versus similar ensemble pruning methods on a heterogeneous ensemble of classifiers demonstrate that it leads to better predictive performance and small-sized pruned ensemble. One of the reasons of these promising results is more time which our method spends for finding the best models of ensemble compared with rivals.