MTSbag: A Method to Solve Class Imbalance Problems

Abstract

Class imbalance is a common problem in classification problems. The Mahalanobis-Taguchi System (MTS) has been shown to be robust in addressing class imbalance problems owing to its inherent properties of classification model construction. The bagging learning approach often has been applied as a superior strategy to reduce the learning bias of classification algorithms. In this study, we propose MTSbag, which integrates the MTS and the bagging-based ensemble learning approaches to enhance the ability of conventional MTS in handling imbalanced data. We perform numerical experiments involving multiple datasets with various class imbalance levels to demonstrate the effectiveness of MTSbag, especially for datasets with high imbalance levels.