# SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks

arXiv: https://arxiv.org/abs/2003.02556 · HTML: https://mlsanity.com/paper/2003.02556/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [OpenFE: Automated Feature Generation with Expert-level Performance](https://mlsanity.com/paper/2211.12507/) (2022)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction](https://mlsanity.com/paper/2410.12865/) (2024)
- [Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers](https://mlsanity.com/paper/2411.01645/) (2024)
- [Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data](https://mlsanity.com/paper/2310.02540/) (2023)
- [FeatAug: Automatic Feature Augmentation From One-to-Many Relationship Tables](https://mlsanity.com/paper/2403.06367/) (2024)
- [Statistical Test for Auto Feature Engineering by Selective Inference](https://mlsanity.com/paper/2410.19768/) (2024)
- [Tabular Feature Discovery With Reasoning Type Exploration](https://mlsanity.com/paper/2506.20357/) (2025)
- [Toward Efficient Automated Feature Engineering](https://mlsanity.com/paper/2212.13152/) (2022)
- [LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning](https://mlsanity.com/paper/2601.20375/) (2026)
- [DNN2LR: Automatic Feature Crossing for Credit Scoring](https://mlsanity.com/paper/2102.12036/) (2021)
- [ALT: An Automatic System for Long Tail Scenario Modeling](https://mlsanity.com/paper/2305.11390/) (2023)
- [A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Data](https://mlsanity.com/paper/2311.12825/) (2023)
- [Leveraging Knowlegde Graphs for Interpretable Feature Generation](https://mlsanity.com/paper/2406.00544/) (2024)
- [Learned Feature Importance Scores for Automated Feature Engineering](https://mlsanity.com/paper/2406.04153/) (2024)
- [SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering via Quality-Aware Candidate-Space Reduction](https://mlsanity.com/paper/2604.27025/) (2026)
- [RelICL: Training-free Relational Learning with Tabular Foundation Models](https://mlsanity.com/paper/2610.01725/) (2026)

## Abstract (arXiv)

Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems. Recently, a growing effort has been made to the development of automatic feature engineering methods, so that the substantial and tedious manual effort can be liberated. However, for industrial tasks, the efficiency and scalability of these methods are still far from satisfactory. In this paper, we proposed a staged method named SAFE (Scalable Automatic Feature Engineering), which can provide excellent efficiency and scalability, along with requisite interpretability and promising performance. Extensive experiments are conducted and the results show that the proposed method can provide prominent efficiency and competitive effectiveness when comparing with other methods. What's more, the adequate scalability of the proposed method ensures it to be deployed in large scale industrial tasks.