Comparative Analysis of Convex Space Learning and SMOTE for Minority Class F1-Scores in Multimodal Tabular Data

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data

Research goal: How does convex space learning for oversampling compare to standard SMOTE in improving F1-scores for minority classes within multimodal tabular datasets?

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