Abstract: The most critical issue in real world applications are class imbalance problems. Imbalanced data sets are common across different domain including banking, health care, finance and other. When such data sets are trained on typical classification algorithm they tends to be biased towards the majority class. The learning task becomes more challenging when there is also an overlap of instances from different classes. In this paper, we propose an undersampling framework for binary classification datasets by removing overlapped data points called Critical Instances Removal based Under-Sampling (CIRUS). Our method is designed to identify and eliminate majority class instances from the overlapping region. Accurate identification and elimination of these instances maximise the visibility of the minority class instances and at the same time minimises excessive elimination of data, which reduces loss of information. Extensive experiments using simulated and real-world datasets were carried out and the results show comparable performance with state-of-the-art methods across different common metrics with exceptional and statistically significant improvements in sensitivity.
Keywords: Imbalanced dataset, undersampling, k-NN, class overlap, classification
