Imbalanced data: a concise survey

Abstract

Imbalanced data affects a wide range of applications in machine learning. As a results, it has garnered a substantial amount of interest from the research community. Given the large volume of research related to imbalanced data, there is a need to organize and analyze the literature to facilitate the progress of the field. In this paper, we provide a state-of-the-art survey of approaches to dealing with imbalanced data. We discuss the existing methods along with their advantages and disadvantages. The presented survey will help researchers to better orient in the vast landscape of imbalanced data research.