GANs in the Panorama of Synthetic Data Generation Methods

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Vaz, B (Autor) Outra; Figueira, A (Autor) FCUP

Revista

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Título: ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS

Vol. 21

ISSN: 1551-6857

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 28