Improving the effectiveness of RBF classifier based on a hybrid cost function

Authors:
D. Huang;T. W. S. Chow
Affiliations:
City University of Hong Kong, Electronic Department, Hong Kong, China;City University of Hong Kong, Electronic Department, Hong Kong, China
Venue:
Neural Computing and Applications
Year:
2007

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Dual-population based coevolutionary algorithm for designing RBFNN with feature selection

Expert Systems with Applications: An International Journal

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Abstract

This paper presents a new minimum classification error (MCE)–mean square error (MSE) hybrid cost function to enhance the classification ability and speed up the learning process of radial basis function (RBF)-based classifier. Contributed by the MCE function, the proposed cost function enables the RBF-based classifier to achieve an excellent classification performance compared with the conventional MSE function. In addition, certain learning difficulties experienced by the MCE algorithm can be solved in an efficient and simple way. The presented results show that the proposed method exhibits a substantially higher convergence rate compared with the MCE function.