Dimensionality reduction for evolving RBF networks with particle swarms

Abstract

Dimensionality reduction including both feature selection and feature extraction techniques are useful for improving the performance of neural networks. In this paper, particle swarm optimization (PSO) algorithm was proposed for simultaneous feature extraction and feature selection, First PSO was used to simultaneous feature extraction and selection in conjunction with k-nearest-neighbor (k-NN) for individual fitness evaluation. With the derived feature set, PSO was then used to evolve RBF networks dynamically. Experimental results on four datasets show that RBF networks evolved with the derived feature set by our proposed algorithm have more simple architecture and stronger generalization ability with the similar classification performance when compared with the networks evolved with the full feature set. © Springer-Verlag Berlin Heidelberg 2006.