Comparative F1-Score Analysis of Tabular Foundation Models Fine-Tuned on SCM versus Diffusion-Augmented Data for Imbalanced

Abstract

Abstract: This paper proposes a novel data augmentation scheme called the conditional generative adversarial network minority-class-augmented oversampling scheme (CTGAN-MOS) for solving class imbalance problems. Our methodology encompassed six key steps: data engineering using sophisticated pre-processing techniques, identifying the type of vulnerabilities present in the data, curating good quality synthetic data using the CTGAN model, the intelligent fusion of real and synthetic data, noise removal from the augmented data using coin-throwing algorithm, and building classifiers with the high-quality aug

Research Question

How does the F1-score of tabular foundation models fine-tuned on SCM-generated synthetic data compare to those using diffusion-based augmentation when evaluated on imbalanced datasets like Adult and Credit?

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