Editing training sets from imbalanced data using fuzzy-rough sets

Abstract

In this research, we study several instance selection methods based on rough set theory and propose an approach able to deal with inconsistency caused by noise and imbalanced data. Recent attention has focused on the significant results obtained in selecting instances from noisy data using fuzzy-rough sets. For imbalanced data, fuzzy-rough sets approach is also applied before and after using balancing methods in order to improve classification performance. In this study, we propose an approach that uses different criteria for minority and majority classes in fuzzy-rough instance selection. It thus eliminates the step of using balancing techniques employed in controversial approach. We also carry out some experiments, measure classification performance and make comparisons with other methods.