What is the comparative impact of overlap class augmentation versus standard SMOTE on the F1-score of minority

Abstract

Abstract: Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbalance. Synthetic data generation, i.e., oversampling, is a common remedy used to improve classifier performance. State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can be used to generate synthetic samples from the convex space of the minority class to improve classifier performance in such cases. Deep generative networks are common deep learning approaches for synthetic sample gen

Research Question

What is the comparative impact of overlap class augmentation versus standard SMOTE on the F1-score of minority classes when training deep generative models for imbalanced tabular classification?

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