A new supervised classification of credit approval data via the hybridized RBF neural network model using information complexity

Abstract

In this paper, we introduce a new approach for supervised classification to handle mixed-data (i.e., categorical, binary, and continuous) data structures using a hybrid radial basis function neural networks (HRBF-NN). HRBF-NN supervised classification combines regression trees, ridge regression, and the genetic algorithm (GA) with radial basis function (RBF) neural networks (NN) along with information complexity (ICOMP) criterion as the fitness function to carry out both classification and subset selection of best predictors which discriminate between the classes. In this manner, we reduce the dimensionality of the data and at the same time improve classification accuracy of the fitted predictive model. We apply HRBFNN supervised classification to a real benchmark credit approval mixed-data set to classify the customers into good/bad classes for credit approval. Our results show the excellent performance of HRBF-NN method in supervised classification tasks.