View article

Rapid data growth presents many challenges for Machine Learning (ML) tasks as it can include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most relevant features to the classification task, known as Feature Selection (FS). The main goal of FS techniques is to maximize the performance of a classification task while keeping the number of features to a minimum. In this study, a hybrid metaheuristic model is designed to solve FS problems based on Binary Biogeography Optimization (BBO) followed by the application of Support Vector Machine Recursive Feature Elimination (SVM-RFE), known as BBO-SVM-RFE. The SVM-RFE is embedded into the BBO to improve the quality of the obtained solutions in the mutation operator in order to enhance the exploitation capability as well as striking an adequate balance between exploitation and exploration of the original BBO. The proposed BBO …