Combination methods for ensembles of RBFs

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. © Springer-Verlag Berlin Heidelberg 2005.