A gray wolf algorithm for feature and parameter selection of support vector classification. (27th March 2021)

• Record Type: Journal Article
• Authors: Qasim, Omar Saber; Algamal, Zakariya Yahya
• Is Part Of: International journal of computing science and mathematics. Volume 13:Number 1(2021)
• Page Start: 93
• Page End: 102
• Publication Date: 2021-03-27
• Subjects: feature selection -- GWO -- gray wolf optimisation -- binary GWO -- parameter selection -- SVC -- support vector classification -- metaheuristic algorithms -- classification; Mathematics -- Periodicals; Computer science -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.285
• Languages: English
• ISSNs: 1752-5055

Abstract

In classification problems, there are many data that contain a large number of features, some of which are irrelevant and cause confusion for the classifiers. The support vector classification (SVC) method is one of the most common methods used in classification. Feature selection, together with the parameters setting of SVC, such as the kernel parameter and the penalty parameter, significantly affects the classification performance of the SVC. In this study, the gray wolf optimisation (GWO) algorithm is proposed to improve feature selection and determine the optimal parameter values of SVC simultaneously. Based on several benchmark datasets for diseases, the experimental results show that the proposed method, FOGWO-SVC, is capable in selecting the best features with best parameters determination. Further, the comparative results demonstrate that the FOGWO-SVC is better or comparable than other competitor algorithms in terms of classification accuracy and feature reduction.