A feature selection algorithm for handwritten character recognition

We present a Genetic Algorithm based feature selection approach according to which feature subsets are represented by individuals of an evolving population. Evolution is controlled by a fitness function taking into account statistical properties of the input data in the subspace represented by each individual, and aims to select the smallest feature subset that optimizes class separability. The originality of our method lies particularly in the definition of the evaluation function. The proposed approach has been tested on a standard database of handwritten digits, showing to be effective both for reducing the number of features used and for improving classifier performance.