A GA-based feature selection approach with an application to handwritten character recognition

Abstract

In the framework of handwriting recognition, we present a novel GA–based feature selection algorithm in which feature subsets are evaluated by means of a specifically devised separability index. This index measures statistical properties of the feature subset and does not depends on any specific classification scheme. The proposed index represents an extension of the Fisher Linear Discriminant method and uses covariance matrices for estimating how class probability distributions are spread out in the considered N-dimensional feature space. A key property of our approach is that it does not require any a priori knowledge about the number of features to be used in the feature subset. Experiments have been performed by using three standard databases of handwritten digits and a standard database of handwritten letters, while the solutions found have been tested with different classification methods. The results have been compared with those obtained by using the whole feature set and with those obtained by using standard feature selection algorithms. The comparison outcomes confirmed the effectiveness of our approach.