# Enhancing Classification Performance via Reinforcement Learning for Feature Selection

arXiv: https://arxiv.org/abs/2403.05979 · HTML: https://mlsanity.com/paper/2403.05979/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 9 of 9)

- [Finite-Sample Analysis for SARSA with Linear Function Approximation](https://mlsanity.com/paper/1902.02234/) (2019)
- [AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning](https://mlsanity.com/paper/2008.12001/) (2020)
- [Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop](https://mlsanity.com/paper/2010.02506/) (2020)
- [Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review](https://mlsanity.com/paper/1905.02845/) (2019)
- [Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time](https://mlsanity.com/paper/2305.15546/) (2023)
- [Capsule Networks with Max-Min Normalization](https://mlsanity.com/paper/1903.09662/) (2019)
- [Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot](https://mlsanity.com/paper/2401.11372/) (2024)
- [Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems](https://mlsanity.com/paper/2308.13246/) (2023)
- [Analyze the robustness of three NMF algorithms (Robust NMF with L1 norm, L2-1 norm NMF, L2 NMF)](https://mlsanity.com/paper/2312.01357/) (2023)

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

- [Predicting Barge Presence and Quantity on Inland Waterways using Vessel Tracking Data: A Machine Learning Approach](https://mlsanity.com/paper/2501.00615/) (2024)
- [Quantum Machine Learning for Secondary Frequency Control](https://mlsanity.com/paper/2512.02065/) (2025)

## Abstract (arXiv)

Feature selection plays a crucial role in improving predictive accuracy by identifying relevant features while filtering out irrelevant ones. This study investigates the importance of effective feature selection in enhancing the performance of classification models. By employing reinforcement learning (RL) algorithms, specifically Q-learning (QL) and SARSA learning, this paper addresses the feature selection challenge. Using the Breast Cancer Coimbra dataset (BCCDS) and three normalization methods (Min-Max, l1, and l2), the study evaluates the performance of these algorithms. Results show that QL@Min-Max and SARSA@l2 achieve the highest classification accuracies, reaching 87% and 88%, respectively. This highlights the effectiveness of RL-based feature selection methods in optimizing classification tasks, contributing to improved model accuracy and efficiency.