Exploring interactive attribute reduction via fuzzy complementary entropy for unlabeled mixed data

Abstract

Attribute reduction is one of the important applications in fuzzy rough set theory. However, most attribute reduction methods in fuzzy rough theory mainly focus on removing irrelevant or redundant attributes. There are few reports about the method of considering attribute interaction. For this reason, this paper proposes an interactive attribute reduction method for unlabeled mixed data. First, some uncertainty measures based on fuzzy complementary entropy are further defined. Then, based on the proposed uncertainty measure, the attribute evaluation criteria of maximal information, minimal redundancy, and maximal interactivity are developed respectively. As a result, the evaluation index of the attribute importance is established by using the idea of unsupervised maximal information-minimal redundancy-maximal interactivity. Finally, a corresponding algorithm is designed to select attributes. The experimental results show that the proposed algorithm has better performance.