Introduction of a new parameter for extracting GDRs in rough set theory

Abstract

In this paper, we discuss an improvement of extraction method of generalized dynamic reducts (GDRs).For a given set of subtables generated from the original decision table,we have proposed and implemented a method for extracting attribute subsetsthat can classify all classifiable objects in at least$100\times(1-\epsilon)\%(0\leq\epsilon<1)$of the given subtables, however, we have not confirmed whether the extracted attribute subsets are relative reducts in the given subtables.In this paper, we introduce a new parameter $\beta (0\leq\beta<1)$ and propose a method that extracts attributes subsets that can classify all classifiable objects in at least $100\times(1-\epsilon)\%$ of the subtables and be relative reducts in at least $100\times(1-\beta)\%$ of the subtables.