A comprehensive review on data-level methods for imbalanced data classification

Abstract

Classification is one of the most important tasks in machine learning and data mining. Most of the classifiers are designed for data sets with equally distributed samples among the classes. Therefore, they encounter a problem with classifying imbalanced data in which one or more classes have much fewer samples than the others. Imbalanced data sets are prevalent in the real-world, so addressing this issue is of utmost importance. There have been many methods suggested to solve this problem showing promising results, a category of which is data-level methods being popular for their flexibility. In this paper, our goal is to review data-level methods comprehensively and categorize them from different perspectives. Also, to simplify doing future research in this field, most of the available benchmark imbalanced data sets, software, and toolboxes are introduced. Finally, existing challenges and future works are elaborated.