# AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning

arXiv: https://arxiv.org/abs/2008.12001 · HTML: https://mlsanity.com/paper/2008.12001/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 24 of 24)

- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [AutoField: Automating Feature Selection in Deep Recommender Systems](https://mlsanity.com/paper/2204.09078/) (2022)
- [Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection](https://mlsanity.com/paper/2302.13221/) (2023)
- [Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.07359/) (2021)
- [Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy](https://mlsanity.com/paper/2109.14180/) (2021)
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- [ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems](https://mlsanity.com/paper/2403.12660/) (2024)
- [FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization](https://mlsanity.com/paper/2405.06312/) (2024)
- [UADB: Unsupervised Anomaly Detection Booster](https://mlsanity.com/paper/2306.01997/) (2023)
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- [Feature Interaction Aware Automated Data Representation Transformation](https://mlsanity.com/paper/2309.17011/) (2023)
- [A Comprehensive Survey on Automated Machine Learning for Recommendations](https://mlsanity.com/paper/2204.01390/) (2022)
- [Feature and Instance Joint Selection: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2205.07867/) (2022)
- [Toward Efficient Automated Feature Engineering](https://mlsanity.com/paper/2212.13152/) (2022)
- [Enhancing Classification Performance via Reinforcement Learning for Feature Selection](https://mlsanity.com/paper/2403.05979/) (2024)
- [Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation](https://mlsanity.com/paper/2501.10555/) (2025)
- [Automation and Feature Selection Enhancement with Reinforcement Learning (RL)](https://mlsanity.com/paper/2503.11991/) (2025)
- [Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2504.17356/) (2025)
- [How Usable is Automated Feature Engineering for Tabular Data?](https://mlsanity.com/paper/2508.13932/) (2025)
- [Feature selection based on cluster assumption in PU learning](https://mlsanity.com/paper/2504.12651/) (2025)
- [P-CAFE: Personalized Cost-Aware Incremental Feature Selection For Electronic Health Records](https://mlsanity.com/paper/2508.08646/) (2025)
- [Rethinking Recurrent Neural Networks for Time Series Forecasting: A Reinforced Recurrent Encoder with Prediction-Oriented Proximal Policy Optimization](https://mlsanity.com/paper/2601.03683/) (2026)
- [FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction](https://mlsanity.com/paper/2606.24679/) (2026)

## Abstract (arXiv)

In this paper, we study the problem of balancing effectiveness and efficiency in automated feature selection. Feature selection is a fundamental intelligence for machine learning and predictive analysis. After exploring many feature selection methods, we observe a computational dilemma: 1) traditional feature selection methods (e.g., mRMR) are mostly efficient, but difficult to identify the best subset; 2) the emerging reinforced feature selection methods automatically navigate feature space to explore the best subset, but are usually inefficient. Are automation and efficiency always apart from each other? Can we bridge the gap between effectiveness and efficiency under automation? Motivated by such a computational dilemma, this study is to develop a novel feature space navigation method. To that end, we propose an Interactive Reinforced Feature Selection (IRFS) framework that guides agents by not just self-exploration experience, but also diverse external skilled trainers to accelerate learning for feature exploration. Specifically, we formulate the feature selection problem into an interactive reinforcement learning framework. In this framework, we first model two trainers skilled at different searching strategies: (1) KBest based trainer; (2) Decision Tree based trainer. We then develop two strategies: (1) to identify assertive and hesitant agents to diversify agent training, and (2) to enable the two trainers to take the teaching role in different stages to fuse the experiences of the trainers and diversify teaching process. Such a hybrid teaching strategy can help agents to learn broader knowledge, and, thereafter, be more effective. Finally, we present extensive experiments on real-world datasets to demonstrate the improved performances of our method: more efficient than existing reinforced selection and more effective than classic selection.