# Review of Methods for Handling Class-Imbalanced in Classification Problems

arXiv: https://arxiv.org/abs/2211.05456 · HTML: https://mlsanity.com/paper/2211.05456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [SMOTified-GAN for class imbalanced pattern classification problems](https://mlsanity.com/paper/2108.03235/) (2021)

## Built on by (most cited first, 1 of 1)

- [Kernel-Based Enhanced Oversampling Method for Imbalanced Classification](https://mlsanity.com/paper/2504.09147/) (2025)

## Abstract (arXiv)

Learning classifiers using skewed or imbalanced datasets can occasionally lead to classification issues; this is a serious issue. In some cases, one class contains the majority of examples while the other, which is frequently the more important class, is nevertheless represented by a smaller proportion of examples. Using this kind of data could make many carefully designed machine-learning systems ineffective. High training fidelity was a term used to describe biases vs. all other instances of the class. The best approach to all possible remedies to this issue is typically to gain from the minority class. The article examines the most widely used methods for addressing the problem of learning with a class imbalance, including data-level, algorithm-level, hybrid, cost-sensitive learning, and deep learning, etc. including their advantages and limitations. The efficiency and performance of the classifier are assessed using a myriad of evaluation metrics.