1. A feature selection method used for a system including learning-pattern storage for storing and maintaining a learning pattern including class information, feature-candidate generation for generating a plurality of feature candidates, and feature decision for deciding a feature set suitable for pattern identification from among the plurality of feature candidates generated by the feature-candidate generation, wherein the feature decision, utilizing a computer-readable memory, prepares a predetermined number of sets for causing the learning patterns to transition according to feature values, decides a predetermined feature candidate having a largest amount of mutual information from the class information of the learning patterns, as a first feature of the feature set by calculating the feature value of each of the learning patterns corresponding to the feature candidates, adds weights to each of the learning patterns according to the decided feature, distributes the learning patterns, and causes the learning patterns to transition to a set corresponding to the determined feature in sequence, and decides predetermined feature candidates having a next largest amount of mutual information between the feature value of the learning pattern corresponding to the feature candidate and the class information of the learning pattern in sequence, as next features of the feature set, under the condition that information about the set including the learning patterns and the decided feature are known.