An improved tabular data generator with VAE-GMM integration
Patricia A. Apellániz Juan Parras Santiago Zazo
The rising use of machine learning in various fields requires robust methods
to create synthetic tabular data. Data should preserve key characteristics
while addressing data scarcity challenges. Current approaches based on
Generative Adversarial Networks, such as the state-of-the-art CTGAN model,
struggle with the complex structures inherent in tabular data. These data often
contain both continuous and discrete features with non-Gaussian distributions.
Therefore, we propose a novel Variational Autoencoder (VAE)-based model that
addresses these limitations. Inspired by the TVAE model, our approach
incorporates a Bayesian Gaussian Mixture model (BGM) within the VAE
architecture. This avoids the limitations imposed by assuming a strictly
Gaussian latent space, allowing for a more accurate representation of the
underlying data distribution during data generation. Furthermore, our model
offers enhanced flexibility by allowing the use of various differentiable
distributions for individual features, making it possible to handle both
continuous and discrete data types. We thoroughly validate our model on three
real-world datasets with mixed data types, including two medically relevant
ones, based on their resemblance and utility. This evaluation demonstrates
significant outperformance against CTGAN and TVAE, establishing its potential
as a valuable tool for generating synthetic tabular data in various domains,
particularly in healthcare.