“The Joint Selection of Features and Training Samples using Genetic Algorithms and Its Application to Character Recognition”

In this paper, we propose an improved feature selection method based on genetic algorithms (GA) that selects not only features but also training samples when creating reference patterns. In this method, the chromosome is composed of a set of bits that represents the state of feature selection. The reference pattern in each category is created from the selected training samples, and the discrimination rate for all training samples is used as the fitness value in GA. GA is used as a tool to find the suboptimal combination that increases the discrimination rate from the enormous number of combinations of features and training samples. We perform experiments using five sets of hand-written Kanji character patterns. Each set consists of patterns in two similar categories. The results show that the proposed method, which selects features and training samples, provides higher recognition rates than the conventional method that selects only features.