A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

TL;DR

This survey reviews various methods for addressing class imbalance in machine learning, analyzing their architectures, effectiveness, and experimental results across multiple studies from 2003 to 2019.

Contribution

It provides a comprehensive overview of single, hybrid, and ensemble methods for class imbalance, including statistical analysis and dataset evaluations.

Findings

Ensemble methods show promising improvements in imbalanced classification.

Hybrid approaches often outperform single-method techniques.

The survey highlights trends and gaps in current research on class imbalance.

Abstract

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.