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.
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Martínez-Muñoz, G., Sánchez-Martínez, A., Hernández-Lobato, D., Suárez, A. (2006). Building Ensembles of Neural Networks with Class-Switching. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_19
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DOI: https://doi.org/10.1007/11840817_19
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