An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems

Abstract

Data imbalance is a common problem in most practical classification applications of machine learning, and it may lead to classification results that are biased towards the majority class if not dealt with properly. An effective means of solving this problem is undersampling in the borderline area; however, it is difficult to find the area that fits the classification boundary. In this paper, we present a novel undersampling framework, whereby the clustering of samples in the majority class is conducted and segmentation is then performed in the boundary area according to the clusters obtained; this enables a better shape that fits the classification boundary to be obtained via the performance of random sampling in the borderline area of these segments. In addition, we hypothesize that there exists an optimal number of classifiers to be integrated into the method of ensemble learning that utilizes multiple classifiers that have been obtained via sampling to promote the algorithm. After passing the hypothesis test, we apply the improved algorithm to the newly developed method. The experimental results show that the proposed method works well.