An ensemble learning approach based on rough set preserving the qualities of approximations

Abstract

In this paper, we confirm the effects of ensemble learning approaches in classification problems based on rough set. Furthermore, we propose an ensemble learning approach based on rough set preserving the qualities of approximations. The proposed method stands on a policy that subsets of attributes whose quality of lower approximation is less than the threshold value is not tolerate. We carried out numerical experiments in order to confirm the classification performance of the proposed method and confirmed its effectiveness.