Enhancing Classification Performance via Reinforcement Learning for Feature Selection
This work addresses feature selection for classification tasks, but it is incremental as it applies existing RL methods to a specific dataset with minor variations.
The study tackled the problem of feature selection to improve classification accuracy by using reinforcement learning algorithms like Q-learning and SARSA on the Breast Cancer Coimbra dataset, achieving accuracies of 87% and 88% with specific normalization methods.
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.