  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.
