A comparison of combination methods for ensembles of RBF networks

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. 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 methods 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 performing methods are Borda count, weighted average and majority voting.