Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations

• URL: http://arxiv.org/abs/2411.10982v2
• Date: Tue, 19 Nov 2024 21:20:47 GMT
• Title: Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
• Authors: Yueyang Shen, Agus Sudjianto, Arun Prakash R, Anwesha Bhattacharyya, Maorong Rao, Yaqun Wang, Joel Vaughan, Nengfeng Zhou,
• Abstract summary: The framework is applied to high dimensional simulated credit scoring data which parallels real-life financial applications. We show that the method is simplistic, guarantees interpretability all the way through, does not require extra tuning and provide unique benefits.
• Score: 0.7227323884094953
• License:
• Abstract: We propose and study a minimalist approach towards synthetic tabular data generation. The model consists of a minimalistic unsupervised SparsePCA encoder (with contingent clustering step or log transformation to handle nonlinearity) and XGboost decoder which is SOTA for structured data regression and classification tasks. We study and contrast the methodologies with (variational) autoencoders in several toy low dimensional scenarios to derive necessary intuitions. The framework is applied to high dimensional simulated credit scoring data which parallels real-life financial applications. We applied the method to robustness testing to demonstrate practical use cases. The case study result suggests that the method provides an alternative to raw and quantile perturbation for model robustness testing. We show that the method is simplistic, guarantees interpretability all the way through, does not require extra tuning and provide unique benefits.

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