# Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings

arXiv: https://arxiv.org/abs/2306.13929 · HTML: https://mlsanity.com/paper/2306.13929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection](https://mlsanity.com/paper/1902.09967/) (2019)
- [Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents](https://mlsanity.com/paper/1805.01542/) (2018)
- [Synthetic Data Generation for Economists](https://mlsanity.com/paper/2011.01374/) (2020)
- [Augmentations: An Insight into their Effectiveness on Convolution Neural Networks](https://mlsanity.com/paper/2205.04064/) (2022)
- [Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks](https://mlsanity.com/paper/2205.07124/) (2022)
- [Using Synthetic Data for Conversational Response Generation in Low-resource Settings](https://mlsanity.com/paper/2204.02653/) (2022)
- [Efficient Deep Learning Methods for Identification of Defective Casting Products](https://mlsanity.com/paper/2205.07118/) (2022)
- [Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep Learning](https://mlsanity.com/paper/2205.10745/) (2022)
- [Efficient Neural Net Approaches in Metal Casting Defect Detection](https://mlsanity.com/paper/2208.04150/) (2022)

## Abstract (arXiv)

The present study aimed to address the issue of imbalanced data in classification tasks and evaluated the suitability of SMOTE, ADASYN, and GAN techniques in generating synthetic data to address the class imbalance and improve the performance of classification models in low-resource settings. The study employed the Generalised Linear Model (GLM) algorithm for class balancing experiments and the Random Forest (RF) algorithm for low-resource setting experiments to assess model performance under varying training data. The recall metric was the primary evaluation metric for all classification models. The results of the class balancing experiments showed that the GLM model trained on GAN-balanced data achieved the highest recall value. Similarly, in low-resource experiments, models trained on data enhanced with GAN-synthesized data exhibited better recall values than original data. These findings demonstrate the potential of GAN-generated synthetic data for addressing the challenge of imbalanced data in classification tasks and improving model performance in low-resource settings.