Binary biogeography-based optimization based SVM-RFE for feature selection

• Published in: Applied soft computing Vol. 101; p. 107026
• Main Authors: Albashish, Dheeb, Hammouri, Abdelaziz I., Braik, Malik, Atwan, Jaffar, Sahran, Shahnorbanun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-03-2021
• Subjects: BBO; Curse of dimensionality; Feature selection; Overfitting; SVM-RFE; Underfitting; Feature selection; Curse of dimensionality; SVM-RFE; BBO; Overfitting; Underfitting
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.107026

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-SVM-RFE method for solving FS problems was assessed on eighteen benchmark datasets. Comparative results showed that the BBO-SVM-RFE method outperforms the BBO method and other existing wrapper and filter methods in terms of accuracy and number of selected features. The obtained results reveal the high potentiality of BBO-SVM-RFE in reliably searching the feature space to obtain the optimal combination of features.
•An improved BBO is proposed for feature selection tasks.•Fusing SVM-RFE in the mutation operator improves the exploitation of the BBO.•Selecting the most relevant features in BBO’s habitats by the support vectors.•Superior outperformance of the algorithm in comparison with other algorithms.