An Insight into Rare Class Problem: Analysis and Potential Solutions

Abstract

The class imbalance problem presents an important challenge to the data mining community, in which the number of examples of one class is more than the others. This problem is characterized by a different distribution of cases between all the classes. In this paper, our goal is to study the various challenges of class imbalance problem and provide a comparative study of the current development of research in learning from imbalanced data. We provide a thorough understanding of the nature of the problem, the methods used for data balancing, the learning objectives and assessment metrics used for getting measurable performance, the stated research solutions and the imbalanced problem in multiple classes. This paper highlights the significant opportunities and challenges in the field and provides potential future research directions in the class imbalance problem.