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Mathematical Problems in Engineering
Volume 2017, Article ID 9316713, 12 pages
Research Article

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

1Wenzhou Vocational College of Science and Technology, Wenzhou, Zhejiang 325006, China
2Beijing Entry-Exit Inspection and Quarantine Bureau, Beijing 100026, China
3College of Computer Science and Technology, Jilin University, Changchun 130012, China
4Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
5College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China

Correspondence should be addressed to Huiling Chen; moc.liamg@ulj.gniliuhnehc

Received 27 September 2016; Accepted 15 January 2017; Published 20 February 2017

Academic Editor: Dylan F. Jones

Copyright © 2017 Yan Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.