Knowledge Acquisition from the Data Consisting of Categories Added with Degrees of Conformity. Proposal of Extended Reduct Calculation in Rough Set Theory.

Abstract

Reduct of the rough set theory is recently used as a mean of knowledge acquisition concerned to a cause-effect relationships consisting of attributes of an object as a cause and of classification as an effect. The data of the rough set theory is limited to categorical data. However, kansei data are often ambiguous. Accordingly, a certain observation value might not conform to a certain category but might only conform to within some grade. In order to make the knowledge acquisition be more agreeable to the actual condition, this paper proposes an algorithm of reduct calculation for the categories added with degrees of conformity which is appropriate to the expression of attributes related to kansei.