How does the F1-score of diffusion-based tabular generative models compare to CTGAN when augmenting data for t

Abstract

Abstract: Class imbalance in tabular datasets poses a challenge for machine learning classification tasks, often leading to biased models that underperform in predicting minority class instances. This study presents a comparative analysis of synthetic data generation methods for addressing class imbalance in tabular data. We evaluate four augmentation approaches—Synthetic Minority Over-sampling Technique (SMOTE), Gaussian Copula, Tabular Variational Autoencoder (TVAE), and Conditional Tabular Generative Adversarial Network (CTGAN)—using the University of California Irvine (UCI) Bank Marketing dataset, w

Research Question

How does the F1-score of diffusion-based tabular generative models compare to CTGAN when augmenting data for training LLMs on imbalanced text classification benchmarks using the HAN benchmark?

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