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Published in:
2006 | OriginalPaper | Chapter
Building Ensembles of Neural Networks with Class-Switching
Authors : Gonzalo Martínez-Muñoz, Aitor Sánchez-Martínez, Daniel Hernández-Lobato, Alberto Suárez
Published in: Artificial Neural Networks – ICANN 2006
Publisher: Springer Berlin Heidelberg
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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.