CHSMOTE: Convex hull-based synthetic minority oversampling technique for alleviating the class imbalance problem

Abstract

The class imbalance problem, caused by skewed class distributions, is prevalent in real-world datasets. Generally, classifiers tend to bias toward the majority class, which may cause enormous misclassification costs. SMOTE is a powerful tool for this problem, which achieves a balanced class distribution by generating synthetic minority samples on the line segment between two minority samples. However, it still has some weaknesses, including 1) oversampling less-informative minority samples, 2) generating over-dense synthetic samples, and 3) generating noise. In this paper, we propose a novel and simple convex hull-based SMOTE (CHSMOTE) algorithm to overcome the three weaknesses of SMOTE simultaneously and further alleviate the class imbalance problem. Specifically, CHSMOTE selects the border minority samples as the initial minority samples for oversampling and identifies a sample synthesis area based on the convex hull by checking whether constructed convex hulls contain border majority samples. CHSMOTE can generate more samples with effective information by properly enlarging the generation range of synthetic samples. Extensive experiment results demonstrate the effectiveness and superiority of the proposed algorithm for alleviating the class imbalance problem.