Enhancing Performance of MLP/RBF Neural Classifiers via an Multivariate Data Distribution Scheme

In this study, the performance of two neural classifiers; namely Multi Layer Perceptron (MLP) and Radial Basis Fuction (RBF), are compared for a multivariate classification problem. MLP and RBF are two of the most widely neural network architecture in literature for classification and have successfully been employed for a variety of applications. A nonlinear scaling scheme for multivariate data is proposed prior to training process in order to improve the performance of both neural classifiers. Proposed scheme modifies the gaussian multivariate data and produce a uniformly distributed multivariate data. It is shown that the proposed scaling scheme increases the performance of neural classifiers.