Abstract
As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. However there are several methods to construct the ensemble and there are no complete results showing which one could be the most appropriate. In this paper we present a comparison of eleven different methods. We have trained ensembles of 3, 9, 20 and 40 networks to show results in a wide spectrum of values. The results show that the improvement in performance above 9 networks in the ensemble depends on the method but it is usually marginal. Also, the best method is called “Decorrelated” and uses a penalty term in the usual Backpropagation function to decorrelate the network outputs in the ensemble.
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Fernández-Redondo, M., Hernández-Espinosa, C., Torres-Sospedra, J. (2004). Multilayer Feedforward Ensembles for Classification Problems. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_114
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DOI: https://doi.org/10.1007/978-3-540-30499-9_114
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