A Semi-Online Training Algorithm for the Radial Basis Function Neural Networks: Applications to Bankruptcy Prediction in Banks

Abstract

This chapter presents an algorithm to train radial basis function neural networks (RBFN) in a semi-online manner. It employs the online, evolving clustering algorithm of Kasabov and Song (2002) in the unsupervised training part of the RBFN and the ordinary least squares estimation technique for the supervised training part. Its effectiveness is demonstrated on two problems related to bankruptcy prediction in financial engineering. In all the cases, 10-fold cross validation was performed. The present algorithm, implemented in two variants, yielded more sensitivity compared to the multi layer perceptron trained by backpropagation (MLP) algorithm over all the problems studied. Based on the results, it can be inferred that the semi-online RBFN without linear terms is better than other neural network techniques. By taking the Area Under the ROC curve (AUC) as the performance metric, the proposed algorithms viz., semi-online RBFN with and without linear terms are compared with classifiers such as ANFIS, TreeNet, SVM, MLP, Linear RBF, RSES and Orthogonal RBF. Out of them TreeNet outperformed both the variants of the semi-online RBFN in both data sets considered here.