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

arXiv: https://arxiv.org/abs/2501.10555 · HTML: https://mlsanity.com/paper/2501.10555/

## 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 (most cited first, 30 of 41)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [An Overview of Deep Semi-Supervised Learning](https://mlsanity.com/paper/2006.05278/) (2020)
- [Feature Engineering for Predictive Modeling using Reinforcement Learning](https://mlsanity.com/paper/1709.07150/) (2017)
- [The autofeat Python Library for Automated Feature Engineering and Selection](https://mlsanity.com/paper/1901.07329/) (2019)
- [Deep Polynomial Neural Networks](https://mlsanity.com/paper/2006.13026/) (2020)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications](https://mlsanity.com/paper/1904.12857/) (2019)
- [Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction](https://mlsanity.com/paper/2205.14526/) (2022)
- [One button machine for automating feature engineering in relational databases](https://mlsanity.com/paper/1706.00327/) (2017)
- [Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing](https://mlsanity.com/paper/2309.04612/) (2023)
- [Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection](https://mlsanity.com/paper/2302.13221/) (2023)
- [Dynamic and Adaptive Feature Generation with LLM](https://mlsanity.com/paper/2406.03505/) (2024)
- [Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents](https://mlsanity.com/paper/2212.13402/) (2022)
- [AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning](https://mlsanity.com/paper/2008.12001/) (2020)
- [Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective](https://mlsanity.com/paper/2306.16893/) (2023)
- [Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions](https://mlsanity.com/paper/2309.13618/) (2023)
- [Feature Selection as Deep Sequential Generative Learning](https://mlsanity.com/paper/2403.03838/) (2024)
- [Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning](https://mlsanity.com/paper/2405.16879/) (2024)
- [Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy](https://mlsanity.com/paper/2109.14180/) (2021)
- [Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop](https://mlsanity.com/paper/2010.02506/) (2020)
- [Techniques for Automated Machine Learning](https://mlsanity.com/paper/1907.08908/) (2019)
- [Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent](https://mlsanity.com/paper/2009.09230/) (2020)

## Built on by (most cited first, 2 of 2)

- [Efficient Post-Training Refinement of Latent Reasoning in Large Language Models](https://mlsanity.com/paper/2506.08552/) (2025)
- [Evolving Demonstration Optimization for Chain-of-Thought Feature Transformation](https://mlsanity.com/paper/2603.09987/) (2026)

## 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.