CORE: core-based synthetic minority over-sampling and borderline majority under-sampling technique

Class imbalance learning has recently drawn considerable attention among researchers. In this area, a rare class is the class of primary interest from the aim of classification. Unfortunately, traditional machine learning algorithms fail to detect this class because a huge majority class overwhelms a tiny minority class. In this paper, we propose a new technique called CORE to handle the class imbalance problem. The objective of CORE is to strengthen the core of a minority class and weaken the risk of misclassified minority instances nearby the borderline of a majority class. These core and borderline regions are defined by the applicability of a safe level. As a result, a minority class is more crowed and dominant. The experiment shows that CORE can significantly improve the predictive performance of a minority class when its dataset is imbalance.