{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/synthetic-tabular-data-generation-for-class","title":"Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study","arxiv_id":"2409.05215","date":"2024-09-08","proceeding":null,"authors":["Emmanouil Panagiotou","Arjun Roy","Eirini Ntoutsi"],"abstract":"Due to their data-driven nature, Machine Learning (ML) models are susceptible to bias inherited from data, especially in classification problems where class and group imbalances are prevalent. Class imbalance (in the classification target) and group imbalance (in protected attributes like sex or race) can undermine both ML utility and fairness. Although class and group imbalances commonly coincide in real-world tabular datasets, limited methods address this scenario. While most methods use oversampling techniques, like interpolation, to mitigate imbalances, recent advancements in synthetic tabular data generation offer promise but have not been adequately explored for this purpose. To this end, this paper conducts a comparative analysis to address class and group imbalances using state-of-the-art models for synthetic tabular data generation and various sampling strategies. Experimental results on four datasets, demonstrate the effectiveness of generative models for bias mitigation, creating opportunities for further exploration in this direction.","url_abs":"https://arxiv.org/abs/2409.05215v1","url_pdf":"https://arxiv.org/pdf/2409.05215v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"synthetic-tabular-data-generation-for-class","repo_url":"https://github.com/panagiotou/fairaugment","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"tabular-data-generation","task_name":"Tabular Data Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2409.05215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.05215"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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