mlsanityData to 14 Oct 2026

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

arXiv 2501.10555 ↗First posted 17 Jan 2025cs.LG cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 41 of its 180 references that are in the map. First posted on arXiv 17 Jan 2025.

Builds on

41 of 180 references.

References · 41

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29 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Efficient Post-Training Refinement of Latent Reasoning in Large Language Modelscited by 7
  2. 2026Evolving Demonstration Optimization for Chain-of-Thought Feature Transformationcited by 0