Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Decisions and evaluations by hierarchical aggregation of information
Fuzzy Sets and Systems
On the construction and training of reformulated radial basis function neural networks
IEEE Transactions on Neural Networks
Construct support vector machine ensemble to detect traffic incident
Expert Systems with Applications: An International Journal
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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 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.