A novel radial basis function network classifier with centers set by hierarchical clustering

This paper proposes a novel method to construct a radial basis function network (RBFN) classifier. Our contribution consists of two parts. The first one is an incremental hierarchical clustering algorithm for constructing the hidden layer, and the second one is to improve the least mean square error method that calculates the weights between the hidden and the output layers of an RBFN. This paper discusses the effects of incorporating an incremental hierarchical clustering algorithm for constructing an RBFN optimized for data classification applications. The formation of clusters is controlled by the class labels of training samples and therefore the clusters identified are well adapted to the local distributions of training instances. In addition, the incremental framework largely reduces the requirement of memory space when the training data set is large. In regard to the calculation of weights, we employ the regularization theory to solve the singular matrix problem that might happen in determining the optimal weights. Experimental results show that the data classifier constructed is capable of delivering comparable classification accuracy as the support vector machine (SVM) and the kernel density estimation based classifier that we have recently proposed, while enjoying significant execution efficiency in handling data sets that contains a high percentage of redundant training instances.