A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917924231446528 |
|---|---|
| author | Ying, Wangyang Wei, Cong Gong, Nanxu Wang, Xinyuan Bai, Haoyue Malarkkan, Arun Vignesh Dong, Sixun Wang, Dongjie Zhang, Denghui Fu, Yanjie |
| author_facet | Ying, Wangyang Wei, Cong Gong, Nanxu Wang, Xinyuan Bai, Haoyue Malarkkan, Arun Vignesh Dong, Sixun Wang, Dongjie Zhang, Denghui Fu, Yanjie |
| contents | Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial intelligence moves towards a data-centric perspective, improving data quality is essential for enhancing model performance in tabular data-driven applications. This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces. Feature selection aims to identify and retain the most informative attributes, while feature generation constructs new features to better capture complex data patterns. We systematically review existing generative methods for tabular data engineering, analyzing their latest advancements, real-world applications, and respective strengths and limitations. This survey emphasizes how RL-based and generative techniques contribute to the automation and intelligence of feature engineering. Finally, we summarize the existing challenges and discuss future research directions, aiming to provide insights that drive continued innovation in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08828 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective Ying, Wangyang Wei, Cong Gong, Nanxu Wang, Xinyuan Bai, Haoyue Malarkkan, Arun Vignesh Dong, Sixun Wang, Dongjie Zhang, Denghui Fu, Yanjie Machine Learning Artificial Intelligence Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial intelligence moves towards a data-centric perspective, improving data quality is essential for enhancing model performance in tabular data-driven applications. This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces. Feature selection aims to identify and retain the most informative attributes, while feature generation constructs new features to better capture complex data patterns. We systematically review existing generative methods for tabular data engineering, analyzing their latest advancements, real-world applications, and respective strengths and limitations. This survey emphasizes how RL-based and generative techniques contribute to the automation and intelligence of feature engineering. Finally, we summarize the existing challenges and discuss future research directions, aiming to provide insights that drive continued innovation in this field. |
| title | A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.08828 |