Selecting features with genetic algorithm in handwritten digit recognition

In this paper, a feature selection method is described. In a pattern recognition system, a large number of features usually make the realization of an efficient classifier difficult. The redundancy within features is also unavoidable. The method proposed in this paper selects features from the existing feature set according to the mutual information (MI) measurement between classes and features. A genetic algorithm (GA) is used to select the most informative feature subset. Based on the experimental results of handwritten digit recognition, this method can reduce the number of features needed in the recognition process without impairing the performance of the classifier significantly