Toward Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

Authors

• Dongjie Wang, University of Kansas, Lawrence
• Yanyong Huang, Southwestern University of Finance and Economics, Chengdu
• Wangyang Ying, Arizona State University
• Haoyue Bai, Arizona State University
• Nanxu Gong, Arizona State University
• Xinyuan Wang, Arizona State University
• Sixun Dong, Arizona State University
• Tao Zhe, The University of Kansas, Lawrence
• Kunpeng Liu, Portland State UniversityFollow
• Meng Xiao, Chinese Academy of Sciences, Beijing
• multiple additional authors

Published In

ACM Transactions on Knowledge Discovery from Data

Document Type

Article

Publication Date

6-1-2026

Subjects

Data-Centric AI, Tabular Data Transformation, Automated Feature Selection, Automated Feature Generation Survey

Abstract

Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric AI, improving data quality and representation has become essential for enhancing model performance, particularly in applications centered around tabular data. This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement. We provide a systematic review of feature selection methods, which identify and retain the most relevant data attributes, and feature generation approaches, which create new features to simplify the capture of complex data patterns. This survey offers a comprehensive overview of current methodologies through an analysis of recent advancements, practical applications, and the strengths and limitations of these techniques. Finally, we outline open challenges and suggest future perspectives to inspire continued innovation in this field.

Rights

Copyright (c) 2026 The Authors Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

DOI

10.1145/3801742

Persistent Identifier

https://archives.pdx.edu/ds/psu/45203

Citation Details

Wang, D., Huang, Y., Ying, W., Bai, H., Gong, N., Wang, X., Dong, S., Zhe, T., Liu, K., Xiao, M., Wang, P., Wang, P., Xiong, H., & Fu, Y. (2026). Toward Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation. ACM Transactions on Knowledge Discovery from Data, 20(5), 1–40.