# BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions

arXiv: https://arxiv.org/abs/2305.09777 · HTML: https://mlsanity.com/paper/2305.09777/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 16 May 2023.

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

- [Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning](https://mlsanity.com/paper/2008.09202/) (2020)
- [TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks](https://mlsanity.com/paper/2109.00666/) (2021)
- [SMOTified-GAN for class imbalanced pattern classification problems](https://mlsanity.com/paper/2108.03235/) (2021)
- [Class Imbalance Problem in Data Mining Review](https://mlsanity.com/paper/1305.1707/) (2013)
- [GenSample: A Genetic Algorithm for Oversampling in Imbalanced Datasets](https://mlsanity.com/paper/1910.10806/) (2019)
- [Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning](https://mlsanity.com/paper/1904.07686/) (2019)
- [Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO](https://mlsanity.com/paper/2210.12870/) (2022)

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

- [Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning](https://mlsanity.com/paper/2503.18569/) (2025)

## Abstract (arXiv)

Class imbalanced problems (CIP) are one of the potential challenges in developing unbiased Machine Learning (ML) models for predictions. CIP occurs when data samples are not equally distributed between the two or multiple classes. Borderline-Synthetic Minority Oversampling Techniques (SMOTE) is one of the approaches that has been used to balance the imbalance data by oversampling the minor (limited) samples. One of the potential drawbacks of existing Borderline-SMOTE is that it focuses on the data samples that lay at the border point and gives more attention to the extreme observations, ultimately limiting the creation of more diverse data after oversampling, and that is the almost scenario for the most of the borderline-SMOTE based oversampling strategies. As an effect, marginalization occurs after oversampling. To address these issues, in this work, we propose a hybrid oversampling technique by combining the power of borderline SMOTE and Generative Adversarial Network to generate more diverse data that follow Gaussian distributions. We named it BSGAN and tested it on four highly imbalanced datasets: Ecoli, Wine quality, Yeast, and Abalone. Our preliminary computational results reveal that BSGAN outperformed existing borderline SMOTE and GAN-based oversampling techniques and created a more diverse dataset that follows normal distribution after oversampling effect.