Multi-objective attribute reduction in three-way decision-theoretic rough set model

Abstract

Attribute reduction plays an important role in rough set theory. Many attribute reduction methods have been proposed based on different definitions of attribute reduct, while an attribute reduct can be regarded as a minimal attribute subset that satisfies specific criteria. Most reducts are defined on the basis of a single criterion, which can only affect one specific characteristic of the data or one preference of users. However, the single criterion-based attribute reduct may not meet the requirement of complex problems. To address this problem, based on three-way decision-theoretic rough set model, this paper defines a multi-objective attribute reduct. Three types of criteria for defining attribute reduct including the positive region, decision cost and mutual information are considered and combined to a multi-objective optimization problem. Based on the proposed multi-objective attribute reduct, we also introduce a multi-objective optimization-based attribute reduction method and an ensemble learning-based attribute reduction method. Experimental results on several datasets show that the proposed attribute reduction methods can obtain a robust and better classification performance.