Construction of RBF Classifiers with Tunable Units using Orthogonal Forward Selection Based on Leave-One-Out Misclassification Rate

Abstract

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) misclassification rate is proposed for the construction of radial basis function (RBF) classifiers with tunable units. Each stage of the construction process determines a RBF unit, namely its centre vector and diagonal covariance matrix as well as weight, by minimising the LOO statistics. This OFS-LOO algorithm is computationally efficient and it is capable of constructing parsimonious RBF classifiers that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF classifier construction procedure is demonstrated using three classification benchmark examples.