A novel learning algorithm for data classification with radial basis function networks

Abstract

This paper proposes a novel learning algorithm for constructing data classifiers with radial basis function (RBF) networks. The RBF networks constructed with the proposed learning algorithm generally are able to deliver the same level of classification accuracy as the support vector machines (SVM). One important advantage of the proposed learning algorithm, in comparison with the support vector machines, is that the proposed learning algorithm normally takes far less time to figure out optimal parameter values with cross validation. A comparison with the SVM is of interest, because it has been shown in a number of recent studies that the SVM generally is able to deliver higher level of accuracy than the other existing data classification algorithms. The proposed learning algorithm works by constructing one RBF network to approximate the probability density function of each class of objects in the training data set. The main distinction of the proposed learning algorithm is how it exploits local distributions of the training samples in determining the optimal parameter values of the basis functions. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software.