MIAMI: MIxed Data Augmentation MIxture

Robin Fuchs, Denys Pommeret, Samuel Stocksieker

Abstract: Performing data augmentation for mixed datasets remains an open challenge. We propose an adaptation of the Mixed Deep Gaussian Mixture Models (MDGMM) to generate such complex data. The MDGMM explicitly handles the different data types and learns a continuous latent representation of the data that captures their dependence structure and can be exploited to conduct data augmentation. We test the ability of our method to simulate crossings of variables that were rarely observed or unobserved during training. The performances are compared with recent competitors relying on Generative Adversarial Networks, Random Forest, Classification And Regression Trees, or Bayesian networks on the UCI Adult dataset.