Method for calculating first-order derivative based feature saliency information in a trained neural network and its application to handwritten digit recognition

A generalised method is presented for calculating the first-order derivative relationship between inputs and outputs in a trained neural network and the use of these derivatives to perform feature selection. We use a handwritten digit data set as a source for comparing this feature selection method with a standard genetic algorithm feature selection method.