ˆβ-QuickReduct: A Heuristic Algorithm for Computing a ˆβ-Reduct Candidate in Variable Precision Rough Set Model

Abstract

In this paper, we propose a heuristic algorithm for computing a ˆβ-reduct candidate in variable precision rough set model (VPRS). In VPRS, there are many definitions of attribute reduction and, for example, β-reduct, Lβ-reduct, and structure-enhancing approach are well known. Nakahama et al. proposed an approach of attribute reduction, called extraction of ˆβ-reduct, as an improvement of β-reduct, and proposed a heuristic algorithm for computing ˆβ-reduct candidates. However, there are some issues based on computation costs. In this paper, we propose a heuristic algorithm called ˆβ-QuickReduct for computing a ˆβ-reduct candidate based on QuickReduct algorithm in Pawlak rough set theory.