# SMOTified-GAN for class imbalanced pattern classification problems

arXiv: https://arxiv.org/abs/2108.03235 · HTML: https://mlsanity.com/paper/2108.03235/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 70 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Minority Oversampling](https://mlsanity.com/paper/1903.09730/) (2019)
- [Langevin-gradient parallel tempering for Bayesian neural learning](https://mlsanity.com/paper/1811.04343/) (2018)
- [Revisiting Bayesian Autoencoders with MCMC](https://mlsanity.com/paper/2104.05915/) (2021)
- [Bayesian graph convolutional neural networks via tempered MCMC](https://mlsanity.com/paper/2104.08438/) (2021)
- [Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis](https://mlsanity.com/paper/2008.03071/) (2020)
- [Guided parallelized stochastic gradient descent for delay compensation](https://mlsanity.com/paper/2101.07259/) (2021)

## Built on by (most cited first, 20 of 20)

- [A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation](https://mlsanity.com/paper/2304.02858/) (2023)
- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [Evaluating the Impact of Data Augmentation on Predictive Model Performance](https://mlsanity.com/paper/2412.02108/) (2024)
- [A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs](https://mlsanity.com/paper/2310.13161/) (2023)
- [Data Distribution-based Curriculum Learning](https://mlsanity.com/paper/2402.07352/) (2024)
- [SMOGAN: Synthetic Minority Oversampling with GAN Refinement for Imbalanced Regression](https://mlsanity.com/paper/2504.21152/) (2025)
- [Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO](https://mlsanity.com/paper/2210.12870/) (2022)
- [Detecting fake accounts through Generative Adversarial Network in online social media](https://mlsanity.com/paper/2210.15657/) (2022)
- [Review of Methods for Handling Class-Imbalanced in Classification Problems](https://mlsanity.com/paper/2211.05456/) (2022)
- [Balanced Split: A new train-test data splitting strategy for imbalanced datasets](https://mlsanity.com/paper/2212.11116/) (2022)
- [BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions](https://mlsanity.com/paper/2305.09777/) (2023)
- [A Bilevel Optimization Framework for Imbalanced Data Classification](https://mlsanity.com/paper/2410.11171/) (2024)
- [Spatiotemporal deep learning models for detection of rapid intensification in cyclones](https://mlsanity.com/paper/2506.08397/) (2025)
- [A Machine Learning Framework for Handling Unreliable Absence Label and Class Imbalance for Marine Stinger Beaching Prediction](https://mlsanity.com/paper/2501.11293/) (2025)
- [Sampling Imbalanced Data with Multi-objective Bilevel Optimization](https://mlsanity.com/paper/2506.11315/) (2025)
- [Regression Augmentation With Data-Driven Segmentation](https://mlsanity.com/paper/2508.01455/) (2025)
- [Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification](https://mlsanity.com/paper/2509.11511/) (2025)
- [Extreme value forecasting using relevance-based data augmentation with deep learning models](https://mlsanity.com/paper/2510.02407/) (2025)
- [Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection](https://mlsanity.com/paper/2605.21164/) (2026)
- [Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift](https://mlsanity.com/paper/2605.21507/) (2026)

## Abstract (arXiv)

Class imbalance in a dataset is a major problem for classifiers that results in poor prediction with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive training dataset. Generally, the pre-processing technique of oversampling of minority class(es) are used to overcome this deficiency. Our focus is on using the hybridization of Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalanced problems. We propose a novel two-phase oversampling approach involving knowledge transfer that has the synergy of SMOTE and GAN. The unrealistic or overgeneralized samples of SMOTE are transformed into realistic distribution of data by GAN where there is not enough minority class data available for GAN to process them by itself effectively. We named it SMOTified-GAN as GAN works on pre-sampled minority data produced by SMOTE rather than randomly generating the samples itself. The experimental results prove the sample quality of minority class(es) has been improved in a variety of tested benchmark datasets. Its performance is improved by up to 9% from the next best algorithm tested on F1-score measurements. Its time complexity is also reasonable which is around O(N2d2T) for a sequential algorithm.