Optimal subclasses with dichotomous variables for feature selection and discrimination

The authors present an efficient algorithm for finding optimal subclasses of a class whose members are represented by several dichotomous features with 0 or 1. Each subclass is expressed by a logical formula with common features among its members. It is shown that some typical subclasses, which contain a large number of samples from a class, consist of a few features. Thus one can select these features as a small subset of all features in problems of feature selection. The selection of best subclasses, when subclasses found by the algorithm is a moderate size, is discussed