A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective
Journal:
arXiv
Published Date:
Feb 12, 2025
Abstract
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.
