A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem
This survey aims to provide an overview of existing methods for researchers and practitioners dealing with class imbalance problems in machine learning.
This survey paper reviews 24 studies from 2003-2019 on methods for managing data imbalance problems, focusing on single, hybrid, and ensemble method designs. It provides a statistical analysis of classification algorithms, experimental conditions, and datasets used in these studies.
The problem of class imbalance is extensive for focusing on numerous applications in the real world. In such a situation, nearly all of the examples are labeled as one class called majority class, while far fewer examples are labeled as the other class usually, the more important class is called minority. Over the last few years, several types of research have been carried out on the issue of class imbalance, including data sampling, cost-sensitive analysis, Genetic Programming based models, bagging, boosting, etc. Nevertheless, in this survey paper, we enlisted the 24 related studies in the years 2003, 2008, 2010, 2012 and 2014 to 2019, focusing on the architecture of single, hybrid, and ensemble method design to understand the current status of improving classification output in machine learning techniques to fix problems with class imbalances. This survey paper also includes a statistical analysis of the classification algorithms under various methods and several other experimental conditions, as well as datasets used in different research papers.