Pattern Classification via Multi-objective Evolutionary RBF Networks Ensemble

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This paper considers a pattern classification by the ensemble of evolutionary RBF networks. Mathematical models generally have a dilemma about model complexity, so the structure determination of RBF network can be considered as the multi-objective optimization problem concerning with accuracy and complexity of the model. The set of RBF networks are obtained by multi-objective evolutionary computation, and then RBF network ensemble is constructed of all or some RBF networks at the final generation. Some experiments on the benchmark problem of the pattern classification demonstrate that the RBF network ensemble has comparable generalization ability to conventional ensemble methods.

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