  Generating synthetic samples from the convex space of the minority class is a
popular oversampling approach for imbalanced classification problems. Recently,
deep-learning approaches have been successfully applied to modeling the convex
space of minority samples. Beyond oversampling, learning the convex space of
neighborhoods in training data has not been used to generate entire tabular
datasets. In this paper, we introduce a deep learning architecture
(NextConvGeN) with a generator and discriminator component that can generate
synthetic samples by learning to model the convex space of tabular data. The
generator takes data neighborhoods as input and creates synthetic samples
within the convex space of that neighborhood. Thereafter, the discriminator
tries to classify these synthetic samples against a randomly sampled batch of
data from the rest of the data space. We compared our proposed model with five
state-of-the-art tabular generative models across ten publicly available
datasets from the biomedical domain. Our analysis reveals that synthetic
samples generated by NextConvGeN can better preserve classification and
clustering performance across real and synthetic data than other synthetic data
generation models. Synthetic data generation by deep learning of the convex
space produces high scores for popular utility measures. We further compared
how diverse synthetic data generation strategies perform in the privacy-utility
spectrum and produced critical arguments on the necessity of high utility
models. Our research on deep learning of the convex space of tabular data opens
up opportunities in clinical research, machine learning model development,
decision support systems, and clinical data sharing.
