Building ensembles of neural networks with class-switching

Abstract

This article investigates the properties of ensembles of neural networks, in which each network in the ensemble is constructed using a perturbed version of the training data. The perturbation consists in switching the class labels of a subset of training examples selected at random. Experiments on several UCI and synthetic datasets show that these class-switching ensembles can obtain improvements in classification performance over both individual networks and bagging ensembles. © Springer-Verlag Berlin Heidelberg 2006.