Improvement of an Extraction Method of Pseudo-Generalized Dynamic Reducts in Rough Sets

Abstract

In this paper, we improve Takahashi et al.’s method for extracting pseudo-generalized dynamic reducts (pGDRs) from a decision table with numerous objects and attributes. Takahashi et al.’s method consists of pGDR candidates extraction phase and pGDR confirmation phase using training datasets. However, a parameter ε used in the confirmation phase is required to set appropriately before starting the confirmation phase. Moreover, it is difficult to interrupt the confirmation processes for a pGDR candidate G even though it is expected that G does not satisfy the condition of pGDR. To solve these two issues, a dynamic update method of the parameter ε and an interruption method of the confirmation processes based on binomial test are introduced to the confirmation phase. Moreover, robustness of the extracted pGDRs to test datasets is examined.