# Feature and Instance Joint Selection: A Reinforcement Learning Perspective

arXiv: https://arxiv.org/abs/2205.07867 · HTML: https://mlsanity.com/paper/2205.07867/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 5 of 5)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning](https://mlsanity.com/paper/2008.12001/) (2020)
- [Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.07359/) (2021)
- [Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop](https://mlsanity.com/paper/2010.02506/) (2020)
- [Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent](https://mlsanity.com/paper/2009.09230/) (2020)

## Built on by (most cited first, 3 of 3)

- [Automation and Feature Selection Enhancement with Reinforcement Learning (RL)](https://mlsanity.com/paper/2503.11991/) (2025)
- [A Multi-Component Reward Function with Policy Gradient for Automated Feature Selection with Dynamic Regularization and Bias Mitigation](https://mlsanity.com/paper/2510.09705/) (2025)
- [Integrating Fairness and Explainability in a Multiple Instance Reinforcement Learning System](https://mlsanity.com/paper/2610.00035/) (2026)

## Abstract (arXiv)

Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards the joint selection conducts feature/instance selection coarsely; thus neglecting the latent fine-grained interaction between feature space and instance space. To address this challenge, we propose a reinforcement learning solution to accomplish the joint selection task and simultaneously capture the interaction between the selection of each feature and each instance. In particular, a sequential-scanning mechanism is designed as action strategy of agents, and a collaborative-changing environment is used to enhance agent collaboration. In addition, an interactive paradigm introduces prior selection knowledge to help agents for more efficient exploration. Finally, extensive experiments on real-world datasets have demonstrated improved performances.