Enhancing Classification Performance via Reinforcement Learning for Feature Selection

arXiv:2403.05979v1 Announce Type: new
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 …

abstract accuracy algorithms arxiv classification cs.lg feature features feature selection filtering importance performance predictive q-learning reinforcement reinforcement learning role study type via