# AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications

arXiv: https://arxiv.org/abs/1904.12857 · HTML: https://mlsanity.com/paper/1904.12857/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 30 of 40)

- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [OpenFE: Automated Feature Generation with Expert-level Performance](https://mlsanity.com/paper/2211.12507/) (2022)
- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [AutoField: Automating Feature Selection in Deep Recommender Systems](https://mlsanity.com/paper/2204.09078/) (2022)
- [Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing](https://mlsanity.com/paper/2309.04612/) (2023)
- [TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction](https://mlsanity.com/paper/2108.09127/) (2021)
- [Towards Cross-Table Masked Pretraining for Web Data Mining](https://mlsanity.com/paper/2307.04308/) (2023)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2408.08585/) (2024)
- [XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2104.10907/) (2021)
- [DIWIFT: Discovering Instance-wise Influential Features for Tabular Data](https://mlsanity.com/paper/2207.02773/) (2022)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies](https://mlsanity.com/paper/2503.20394/) (2025)
- [i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems](https://mlsanity.com/paper/2204.00281/) (2022)
- [A Comprehensive Survey on Automated Machine Learning for Recommendations](https://mlsanity.com/paper/2204.01390/) (2022)
- [Uncertainty-Guided Likelihood Tree Search](https://mlsanity.com/paper/2407.03951/) (2024)
- [ChaCha for Online AutoML](https://mlsanity.com/paper/2106.04815/) (2021)
- [Feature Cross Search via Submodular Optimization](https://mlsanity.com/paper/2107.02139/) (2021)
- [FeatAug: Automatic Feature Augmentation From One-to-Many Relationship Tables](https://mlsanity.com/paper/2403.06367/) (2024)
- [Tabular Feature Discovery With Reasoning Type Exploration](https://mlsanity.com/paper/2506.20357/) (2025)
- [AEFE: Automatic Embedded Feature Engineering for Categorical Features](https://mlsanity.com/paper/2110.09770/) (2021)
- [Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation](https://mlsanity.com/paper/2501.10555/) (2025)
- [TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems](https://mlsanity.com/paper/2507.03895/) (2025)
- [TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks](https://mlsanity.com/paper/2606.02384/) (2026)
- [Dual-Agent Reinforcement Learning for Automated Feature Generation](https://mlsanity.com/paper/2505.12628/) (2025)
- [How Usable is Automated Feature Engineering for Tabular Data?](https://mlsanity.com/paper/2508.13932/) (2025)
- [DNN2LR: Automatic Feature Crossing for Credit Scoring](https://mlsanity.com/paper/2102.12036/) (2021)

## Abstract (arXiv)

Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its customers, ranging from banks, hospitals, to Internet corporations. By performing beam search in a tree-structured space, AutoCross enables efficient generation of high-order cross features, which is not yet visited by existing works. Additionally, we propose successive mini-batch gradient descent and multi-granularity discretization to further improve efficiency and effectiveness, while ensuring simplicity so that no machine learning expertise or tedious hyper-parameter tuning is required. Furthermore, the algorithms are designed to reduce the computational, transmitting, and storage costs involved in distributed computing. Experimental results on both benchmark and real-world business datasets demonstrate the effectiveness and efficiency of AutoCross. It is shown that AutoCross can significantly enhance the performance of both linear and deep models.