# A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation

arXiv: https://arxiv.org/abs/2304.02858 · HTML: https://mlsanity.com/paper/2304.02858/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 369 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 30 of 53)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Time Series Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2002.12478/) (2020)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Data Augmentation for Deep Graph Learning: A Survey](https://mlsanity.com/paper/2202.08235/) (2022)
- [An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks](https://mlsanity.com/paper/2007.15951/) (2020)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [L2-constrained Softmax Loss for Discriminative Face Verification](https://mlsanity.com/paper/1703.09507/) (2017)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)

## Built on by (most cited first, 25 of 25)

- [Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network](https://mlsanity.com/paper/2408.10181/) (2024)
- [Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals](https://mlsanity.com/paper/2308.08480/) (2023)
- [Review of deep learning models for crypto price prediction: implementation and evaluation](https://mlsanity.com/paper/2405.11431/) (2024)
- [CiteFusion: An Ensemble Framework for Citation Intent Classification Harnessing Dual-Model Binary Couples and SHAP Analyses](https://mlsanity.com/paper/2407.13329/) (2024)
- [Spatiotemporal deep learning models for detection of rapid intensification in cyclones](https://mlsanity.com/paper/2506.08397/) (2025)
- [Robust-Multi-Task Gradient Boosting](https://mlsanity.com/paper/2507.11411/) (2025)
- [An explainable Recursive Feature Elimination to detect Advanced Persistent Threats using Random Forest classifier](https://mlsanity.com/paper/2511.09603/) (2025)
- [Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing](https://mlsanity.com/paper/2606.01221/) (2026)
- [Random Forest Variable Importance-based Selection Algorithm in Class Imbalance Problem](https://mlsanity.com/paper/2312.10573/) (2023)
- [Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions](https://mlsanity.com/paper/2407.14381/) (2024)
- [Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification](https://mlsanity.com/paper/2409.19214/) (2024)
- [Enhancing Precision of Automated Teller Machines Network Quality Assessment: Machine Learning and Multi Classifier Fusion Approaches](https://mlsanity.com/paper/2501.01067/) (2025)
- [Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies](https://mlsanity.com/paper/2502.06866/) (2025)
- [Convolutional neural networks for mineral prospecting through alteration mapping with remote sensing data](https://mlsanity.com/paper/2502.18533/) (2025)
- [Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI](https://mlsanity.com/paper/2503.17623/) (2025)
- [Imbalanced malware classification: an approach based on dynamic classifier selection](https://mlsanity.com/paper/2504.00041/) (2025)
- [Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises](https://mlsanity.com/paper/2509.00550/) (2025)
- [Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques](https://mlsanity.com/paper/2509.07605/) (2025)
- [Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes](https://mlsanity.com/paper/2509.09960/) (2025)
- [Extreme value forecasting using relevance-based data augmentation with deep learning models](https://mlsanity.com/paper/2510.02407/) (2025)
- [Data Augmentation Supporting a Conversational Agent Designed for Smoking Cessation Support Groups](https://mlsanity.com/paper/2512.17092/) (2025)
- [A Theoretical and Empirical Taxonomy of Imbalance in Binary Classification](https://mlsanity.com/paper/2601.04149/) (2026)
- [Perturb-and-Restore: Simulation-driven Structural Augmentation Framework for Imbalance Chromosomal Anomaly Detection](https://mlsanity.com/paper/2604.00854/) (2026)
- [DADIR: Density-Aware Data-level Imbalanced Regression Framework](https://mlsanity.com/paper/2607.17178/) (2026)
- [Learning under Localized Minority Imbalance](https://mlsanity.com/paper/2610.04936/) (2026)

## Abstract (arXiv)

Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other. Ensemble learning combines multiple models to obtain a robust model and has been prominently used with data augmentation methods to address class imbalance problems. In the last decade, a number of strategies have been added to enhance ensemble learning and data augmentation methods, along with new methods such as generative adversarial networks (GANs). A combination of these has been applied in many studies, and the evaluation of different combinations would enable a better understanding and guidance for different application domains. In this paper, we present a computational study to evaluate data augmentation and ensemble learning methods used to address prominent benchmark CI problems. We present a general framework that evaluates 9 data augmentation and 9 ensemble learning methods for CI problems. Our objective is to identify the most effective combination for improving classification performance on imbalanced datasets. The results indicate that combinations of data augmentation methods with ensemble learning can significantly improve classification performance on imbalanced datasets. We find that traditional data augmentation methods such as the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) are not only better in performance for selected CI problems, but also computationally less expensive than GANs. Our study is vital for the development of novel models for handling imbalanced datasets.