Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915109710856192 |
|---|---|
| author | Wang, Dongjie Huang, Yanyong Ying, Wangyang Bai, Haoyue Gong, Nanxu Wang, Xinyuan Dong, Sixun Zhe, Tao Liu, Kunpeng Xiao, Meng Wang, Pengfei Wang, Pengyang Xiong, Hui Fu, Yanjie |
| author_facet | Wang, Dongjie Huang, Yanyong Ying, Wangyang Bai, Haoyue Gong, Nanxu Wang, Xinyuan Dong, Sixun Zhe, Tao Liu, Kunpeng Xiao, Meng Wang, Pengfei Wang, Pengyang Xiong, Hui Fu, Yanjie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation Wang, Dongjie Huang, Yanyong Ying, Wangyang Bai, Haoyue Gong, Nanxu Wang, Xinyuan Dong, Sixun Zhe, Tao Liu, Kunpeng Xiao, Meng Wang, Pengfei Wang, Pengyang Xiong, Hui Fu, Yanjie Machine Learning Artificial Intelligence 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. |
| title | Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.10555 |