Classification with class imbalance problem: a review

Abstract

Most existing classification approaches assume the underlying training set is evenly distributed. In class imbalanced classification, the training set for one class (majority) far surpassed the training set of the other class (minority), in which, the minority class is often the more interesting class. In this paper, we review the issues that come with learning from imbalanced class data sets and various problems in class imbalance classification. A survey on existing approaches for handling classification with imbalanced datasets is also presented. Finally, we discuss current trends and advancements which potentially could shape the future direction in class imbalance learning and classification. We also found out that the advancement of machine learning techniques would mostly benefit the big data computing in addressing the class imbalance problem which is inevitably presented in many real world applications especially in medicine and social media.