Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912257317797888 |
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| author | Yang, Zeyu Yu, Han Guo, Peikun Zanna, Khadija Yang, Xiaoxue Sano, Akane |
| author_facet | Yang, Zeyu Yu, Han Guo, Peikun Zanna, Khadija Yang, Xiaoxue Sano, Akane |
| contents | Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this research, we introduce a novel tabular diffusion model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes, such as sex and race. The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms existing methods for synthesizing tabular data on fairness metrics such as demographic parity ratio and equalized odds ratio, achieving improvements of over $10\%$. Our implementation is available at https://github.com/comp-well-org/fair-tab-diffusion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08254 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models Yang, Zeyu Yu, Han Guo, Peikun Zanna, Khadija Yang, Xiaoxue Sano, Akane Machine Learning Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this research, we introduce a novel tabular diffusion model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes, such as sex and race. The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms existing methods for synthesizing tabular data on fairness metrics such as demographic parity ratio and equalized odds ratio, achieving improvements of over $10\%$. Our implementation is available at https://github.com/comp-well-org/fair-tab-diffusion. |
| title | Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.08254 |