Fuzzy rough set-based attribute reduction using distance measures

Abstract

Attribute reduction is one of the most important applications of fuzzy rough sets in machine learning and pattern recognition. Most existing methods employ the intersection operation of fuzzy relations to construct the dependency function of attribute reduction. However, the intersection operation may lead to low discrimination of fuzzy decision in high-dimensional data space. In this study, we introduce distance measures into fuzzy rough sets and propose a novel method for attribute reduction. We first construct a fuzzy rough set model based on distance measure with a fixed parameter. Then, the fixed distance parameter is replaced by a variable one to better characterize attribute reduction with fuzzy rough sets. Some iterative formulas for computing fuzzy rough dependency and attribute significance are presented, and an iterative computation model based on a variable distance parameter is proposed. Based on this, a greedy convergent algorithm for attribute reduction is designed. The experimental comparison demonstrates that the proposed reduction algorithm is effective and performs better than some of the other existing algorithms.