An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

Abstract

In order to develop a new and effective prediction system, the full potential of support vector machine (SVM) was explored by using an improved grey wolf optimization (GWO) strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction. In the proposed approach, particle swarm optimization (PSO) was firstly adopted to generate the diversified initial positions, and then GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on SVM. The resultant methodology, IGWO-SVM, is rigorously examined based on the real-life data which includes a series of factors that influence the students’ final decision to choose the specific major. To validate the proposed method, other metaheuristic based SVM methods including GWO based SVM, genetic algorithm based SVM, and particle swarm optimization-based SVM were used for comparison in terms of classification accuracy, AUC (the area under the receiver operating characteristic (ROC) curve), sensitivity, and specificity. The experimental results demonstrate that the proposed approach can be regarded as a promising success with the excellent classification accuracy, AUC, sensitivity, and specificity of 87.36%, 0.8735, 85.37%, and 89.33%, respectively. Promisingly, the proposed methodology might serve as a new candidate of powerful tools for second major selection.