GANs in the Panorama of Synthetic Data Generation Methods

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Vaz, B (Author) Other; Figueira, A (Author) FCUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS

Vol. 21

ISSN: 1551-6857

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-010-C9M

DOI: 10.1145/3657294

Abstract (EN): This article focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning (ML) applications, using fake news detection as a case study. We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models' performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 28