Consistency Based Attribute Reduction

Qinghua Hu, Hui Zhao, Zongxia Xie, Daren Yu

Abstract: Rough sets are widely used in feature subset selection and attribute reduction. In most of the existing algorithms, the dependency function is employed to evaluate the quality of a feature subset. The disadvantages of using dependency are discussed in this paper. And the problem of forward greedy search algorithm based on dependency is presented. We introduce the consistency measure to deal with the problems. The relationship between dependency and consistency is analyzed. It is shown that consistency measure can reflects not only the size of decision positive region, like dependency, but also the sample distribution in the boundary region. Therefore it can more finely describe the distinguishing power of an attribute set. Based on consistency, we redefine the redundancy and reduct of a decision system. We construct a forward greedy search algorithm to find reducts based on consistency. What’s more, we employ cross validation to test the selected features, and reduce the overfitting features in a reduct. The experimental results with UCI data show that the proposed algorithm is effective and efficient.