Feature selection using rough sets theory

Abstract

The paper is related to one of the aspects of learning from examples, namely learning how to identify a class of objects a given object instance belongs to. In the paper a method of generating sequence of features allowing such identification is presented. In this approach examples are represented in the form of attribute-value table with binary values of attributes. The main assumption is that one feature sequence is determined for all possible object instances, that is next feature in the order does not depend on values of the previous features. The algorithm is given generating a sequence under these conditions. Theoretical background of the proposed method is rough sets theory. Some generalizations of this theory are introduced in the paper. Finally, a discussion of the presented approach is provided and results of functioning of the proposed algorithm are summarized. Direction of further research is also indicated.