Missing value imputation in machine learning is the task of estimating the
missing values in the dataset accurately using available information. In this
task, several deep generative modeling methods have been proposed and
demonstrated their usefulness, e.g., generative adversarial imputation
networks. Recently, diffusion models have gained popularity because of their
effectiveness in the generative modeling task in images, texts, audio, etc. To
our knowledge, less attention has been paid to the investigation of the
effectiveness of diffusion models for missing value imputation in tabular data.
Based on recent development of diffusion models for time-series data
imputation, we propose a diffusion model approach called "Conditional
Score-based Diffusion Models for Tabular data" (TabCSDI). To effectively handle
categorical variables and numerical variables simultaneously, we investigate
three techniques: one-hot encoding, analog bits encoding, and feature
tokenization. Experimental results on benchmark datasets demonstrated the
effectiveness of TabCSDI compared with well-known existing methods, and also
emphasized the importance of the categorical embedding techniques.

本文提出了一种用于 Tabular 数据的基于条件分数的扩散模型方法（TabCSDI），并研究了热点问题中的三种处理技术。实验结果表明与现有方法相比，TabCSDI 是有效的，同时也强调了分类嵌入技术的重要性。