Abstract
Building an ensemble of classifiers is an useful way to improve the performance. In the case of neural networks the bibliography has centered on the use of Multilayer Feedforward (MF). However, there are other interesting networks like Radial Basis Functions (RBF) that can be used as elements of the ensemble. Furthermore, as pointed out recently, the network RBF can also be trained by gradient descent, so all the methods of constructing the ensemble designed for MF are also applicable to RBF. In this paper we present the results of using eleven methods to construct a ensemble of RBF networks. The results show that the best method is in general the Simple Ensemble.
This research was supported by project MAPACI TIC2002-02273 of CICYT in Spain
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Hernández-Espinosa, C., Fernández-Redondo, M., Torres-Sospedra, J. (2004). Experiments on Ensembles of Radial Basis Functions. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_25
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DOI: https://doi.org/10.1007/978-3-540-24844-6_25
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