Selecting the Optimal Classifier for Wrapper-Based Feature Selection Methods - Zandi, Farzad; Mansouri, Parvaneh; Sheibani, Reza (Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 2024)
Dimensionality reduction, the elimination of irrelevant features, and the selection of an optimal subset of features are critical components in the construction of an efficacious machine learning model. Among the various feature selection methodologies, wrapper-based methods yield superior results due to their evaluation of candidate subsets. Numerous meta-heuristic methods have been employed in this feature selection process. A significant and complex issue in feature selection utilizing these methods is the selection of the most suitable classifier. In this study, we propose a novel method for selecting the optimal classifier during the feature selection process. We employ ten distinct classifiers for two swarm intelligence methods, namely Bat and Gray Wolf, and compute their results on four cancer datasets: Leukemia, SRBCT, Prostate, and Colon. Our findings demonstrate that the proposed method identifies the optimal classifiers for all four datasets. Consequently, when employing wrapper-based methods to select features for each dataset, the optimal classifier is identified.