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.

翻译：随着机器学习在各领域的广泛应用，亟需稳健的方法来生成合成表格数据。这些数据应在保持关键特征的同时，应对数据稀缺的挑战。当前基于生成对抗网络的方法（如最先进的CTGAN模型）难以处理表格数据固有的复杂结构。此类数据通常包含连续和离散特征，且其分布往往非高斯。为此，我们提出一种基于变分自编码器的新型模型以解决这些局限。受TVAE模型的启发，我们的方法在VAE架构中融入了贝叶斯高斯混合模型。这避免了严格假设潜在空间为高斯分布所带来的限制，从而在数据生成过程中更准确地表征底层数据分布。此外，我们的模型通过允许对单个特征使用多种可微分布，提供了更强的灵活性，使其能够同时处理连续和离散数据类型。我们在三个包含混合数据类型的真实数据集（其中两个与医学相关）上，基于其相似性和实用性对模型进行了全面验证。评估结果表明，该模型显著优于CTGAN和TVAE，证实了其作为跨领域（尤其是医疗健康领域）合成表格数据生成工具的重要潜力。