GANs in the Panorama of Synthetic Data Generation Methods Article Swipe
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3657294
- https://dl.acm.org/doi/pdf/10.1145/3657294
- OA Status
- bronze
- Cited By
- 11
- References
- 56
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- 10
- OpenAlex ID
- https://openalex.org/W4394686888
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394686888Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3657294Digital Object Identifier
- Title
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GANs in the Panorama of Synthetic Data Generation MethodsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-10Full publication date if available
- Authors
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Bruno Vaz, Álvaro FigueiraList of authors in order
- Landing page
-
https://doi.org/10.1145/3657294Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3657294Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3657294Direct OA link when available
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Panorama, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 1Per-year citation counts (last 5 years)
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56Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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