Building ensembles of neural networks with class-switching

Authors:
Gonzalo Martínez-Muñoz;Aitor Sánchez-Martínez;Daniel Hernández-Lobato;Alberto Suárez
Affiliations:
Universidad Autónoma de Madrid, Madrid, Spain;Universidad Autónoma de Madrid, Madrid, Spain;Universidad Autónoma de Madrid, Madrid, Spain;Universidad Autónoma de Madrid, Madrid, Spain
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

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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.