A method to reduce boundary regions in three-way decision theory

Abstract

A method for dealing the boundary region in three-way decision theory is proposed. In the three-way decision theory, all the elements are divided into three regions: positive region, negative region and boundary region. Positive region makes a decision of acceptance, negative region makes a decision of rejection. They can generate certain rules. However, boundary region makes a decision of abstaining. They generate uncertain rule. In classification, we always do with the boundary region. In this paper, we propose a method based on tri-training algorithm to reduce the boundary region. In the tri-training algorithm, we build up three classifiers based on three-way decision. We divide all the data into three parts randomly, aiming to keep the three classifiers different. We adopt a voting mechanism to label test samples. Experiments have shown that in most cases, tri-training algorithm is not only benefit for reducing boundary regions but also for improving classification precision. We also find some rules about the parameters alpha and beta how to affect boundary regions and classification precision.