关键词: 粗糙集, 最小约简, 集合枚举树, 属性重要度, 剪枝

Abstract: The purpose of this paper is to present an effective approach to achieve minimal attribute reduction. To achieve this goal, in this paper, it introduces a set-enumeration search tree by using total sequence over condition attribute set, and proposes a minimal attribute reduction algorithm, which uses the top-down level-first traversal to search set-enumeration search tree and guarantee that reduction discovered by it is a minimal reduction. To realize performance gains, this algorithm uses core and superset pruning to reduce search space, and uses optimal computing to guarantee that positive region of the same set only computes one time for reducing computing time. The experimental results with UCI data show that the proposed algorithm is effective.

Key words: rough set, minimal reduction, set enumeration search tree, attribute significance, pruning