Abstract
Building an ensemble of classifiers is a 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. In a previous paper we presented results of different methods to build the ensemble of RBF. The results showed that the best method is in general the Simple Ensemble. The combination method used in that research was averaging. In this paper we present results of fourteen different combination methods for a simple ensemble of RBF. The best methods are Borda Count, Weighted Average and Majority Voting.
This research was supported by the project MAPACI TIC2002-02273 of CICYT in Spain.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Hernández-Espinosa, C., Torres-Sospedra, J., Fernández-Redondo, M. (2005). Combination Methods for Ensembles of RBFs. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_20
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DOI: https://doi.org/10.1007/11550907_20
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