# A Review of Machine Learning Techniques in Imbalanced Data and Future Trends

arXiv: https://arxiv.org/abs/2310.07917 · HTML: https://mlsanity.com/paper/2310.07917/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 225 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [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)
- [Class Rectification Hard Mining for Imbalanced Deep Learning](https://mlsanity.com/paper/1712.03162/) (2017)
- [Imbalanced Deep Learning by Minority Class Incremental Rectification](https://mlsanity.com/paper/1804.10851/) (2018)
- [Survey of resampling techniques for improving classification performance in unbalanced datasets](https://mlsanity.com/paper/1608.06048/) (2016)
- [Self-paced Ensemble for Highly Imbalanced Massive Data Classification](https://mlsanity.com/paper/1909.03500/) (2019)
- [Deep Reinforcement Learning for Imbalanced Classification](https://mlsanity.com/paper/1901.01379/) (2019)
- [Radial-Based Undersampling for Imbalanced Data Classification](https://mlsanity.com/paper/1906.00452/) (2019)
- [Online Ensemble Learning for Imbalanced Data Streams](https://mlsanity.com/paper/1310.8004/) (2013)
- [Imbalance Learning for Variable Star Classification](https://mlsanity.com/paper/2002.12386/) (2020)
- [Deep Learning for Malicious Flow Detection](https://mlsanity.com/paper/1802.03358/) (2018)
- [Optimizing Cost-Sensitive SVM for Imbalanced Data :Connecting Cluster to Classification](https://mlsanity.com/paper/1702.01504/) (2017)
- [Predicting Louisiana Public High School Dropout through Imbalanced Learning Techniques](https://mlsanity.com/paper/1910.13018/) (2019)

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

- [Constructing balanced datasets for predicting failure modes in structural systems under seismic hazards](https://mlsanity.com/paper/2503.01882/) (2025)

## Abstract (arXiv)

For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain. Real-life problems inspire researchers to navigate and further improve data processing and algorithmic approaches to achieve effective and computationally efficient methods for imbalanced learning. In this paper, we have collected and reviewed 258 peer-reviewed papers from archival journals and conference papers in an attempt to provide an in-depth review of various approaches in imbalanced learning from technical and application perspectives. This work aims to provide a structured review of methods used to address the problem of imbalanced data in various domains and create a general guideline for researchers in academia or industry who want to dive into the broad field of machine learning using large-scale imbalanced data.