Enhancing Classification Performance via Reinforcement Learning for Feature Selection

TL;DR

This paper explores reinforcement learning algorithms, Q-learning and SARSA, for feature selection to improve classification accuracy, demonstrating their effectiveness on the Breast Cancer Coimbra dataset with various normalization methods.

Contribution

It introduces RL-based feature selection methods, specifically Q-learning and SARSA, as novel approaches to enhance classification performance.

Findings

QL@Min-Max achieves 87% accuracy

SARSA@l2 achieves 88% accuracy

RL methods outperform traditional feature selection

Abstract

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.