Mathematical Problems in Engineering

Volume 2017 (2017), Article ID 9316713, 12 pages

https://doi.org/10.1155/2017/9316713

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

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

Correspondence should be addressed to Huiling Chen

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.

#### 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.

#### 1. Introduction

At present, most colleges and universities attach great importance to the needs of students’ diversified development and provide the opportunity of categorization of professional direction for students in the high grade. Therefore, the fact that students choose the suitable major according to their own characteristics is of significant importance, which is the important prerequisite for promoting the career development in the future. However, students are apt to getting lost when they are faced with choosing the major because of a series of factors such as subjective consciousness, randomness, and blindness. Is there scientific and reasonable means to allow students to understand their own characteristics and to find the most suitable major development direction for their own? Colleges have accumulated a large number of data related to student resources during the personnel training process, and a lot of important information and knowledge is hidden in these massive data. We can extract the valuable information from these massive data by using data mining technology to construct a prediction model appropriate for major classification; therefore it can help the students to make the right decision on choosing the major.

So far, the data mining techniques in the instruction and evaluation, behavior analysis of teachers and students, scores analysis and prediction, occupation guidance, and other aspects have more applications, which also have proposed several methods to apply to course selection, major selection, and so on. Cheng et al. [1] proposed an innovative approach that combines a student concept model and the change mining mechanism for analyzing the learning problems of students from their historical assessment data. The experimental results showed that those analysis results provided by the innovative approach were helpful to the teachers in providing appropriate instructional assistance and remedial learning materials for improving the learning achievements of the students. Elbadrawy et al. [2] proposed to predict the students’ performance according to a recommendation system based on personalized analysis. The results showed that the method of multiple regression and improved matrix decomposition could be more timely and accurate to predict the students’ scores in the next term compared with the traditional methods. Ognjanovic et al. [3] proposed an approach for extracting student preferences from sources available in institutional student information systems. The extracted preferences were analyzed using the analytical hierarchy process, which was used for predicting students’ course selection. The results demonstrated that the accuracy was high and equivalent to that of previous data mining approaches using fully identifiable data. Campagni et al. [4] presented a data mining methodology based on clustering and sequential patterns techniques to analyze the careers of university graduated students. The results underlined that the more the students follow the order given by the ideal career, the more they get good performance in terms of graduation time and final grade. Kardan et al. [5] proposed to use neural networks to establish models for analyzing and predicting college students’ network selection. Experimental results showed that the model had higher prediction accuracy than Support Vector Regression, -Nearest Neighborhood, and Decision Tree. Thammasiri et al. [6] discussed the problem of unbalanced distribution and supported the model of vector machine and oversampling data equalization technology, which were used to predict the loss rate of new students. The results showed that this kind of data mining technology would achieve the best classification, making the overall prediction accuracy over 90%. Lee [7] performed logical regression analysis and found that a large number of computer courses learned at the middle school level had a significant impact on STEM subject selection in the US colleges. Huang and Xu [8] analyzed the evaluation theories of psychology and statistics and developed a second major selection system based on a rough set-based association rule mining algorithm. The simulation results demonstrated that their method was accurate for software engineering, networking, and programming majors.

To improve the performance of second major selection, this study proposes an improved support vector machine (SVM) based prediction system. In the proposed approach, an enhanced grey wolf optimization strategy (hereafter IGWO) was established to screen the representative features, and then SVM was employed to perform the prediction task based on the feature subset identified by the IGWO strategy. GWO is a new swarm intelligence method proposed recently by Mirjalili et al. [9]. Due to its great exploration capacity, it has been successfully applied to many practical problems, such as load frequency control of interconnected power system [10], combined heat and power dispatch [11], design of castellated beams [12], and the two-stage assembly flow shop scheduling problem [13]. However, the initial population of original GWO is generated randomly, which may lead to the fact that the grey wolves in search space are lacking diversity. Many studies [14–18] have shown that population initialization may affect the global convergence speed and also the quality of the final solution for swarm intelligence optimization. Moreover, the initial population with good diversity is helpful to promote the performance of optimization algorithm. Inspired by this idea, we use particle swarm optimization (PSO) to generate a diverse initial population and then construct a binary version of GWO to execute feature selection task. One main reason that we have chosen the PSO strategy lies in that the PSO approach is a simple approach with fast search speed and high efficiency compared with others. As shown, the experimental results have shown that PSO have indeed improved the quality of initial population for GWO. At the same time, it is also important to choose an effective and efficient classifier for evaluating the most discriminative features. In this study, the SVM classifier is used to compute the fitness value due to its good generalization capability and excellent performance in many classification tasks [18–23].

The efficacy of the resultant method, the IGWO-SVM based prediction system, was rigorously compared against SVM without feature selection, GWO based SVM (hereafter GWO-SVM), genetic algorithm based SVM (hereafter GA-SVM), and particle swarm optimization-based SVM (hereafter PSO-SVM) on the real-life dataset collected from Wenzhou Vocational College of Science and Technology. The classifiers were compared with respect to the classification accuracy (ACC), sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) criterion. The experimental results demonstrated that the proposed IGWO-SVM approach achieved much better performance than other competitive counterparts.

The rest of this paper is organized as follows. Section 2 offers brief background knowledge on SVM and binary version of GWO and PSO. Section 3 presents the detailed implementation of the proposed method. Section 4 describes the experimental design. Section 5 presents the experimental results and discusses the proposed approach. Finally, Section 6 summarizes the conclusions and recommendations for future work.

#### 2. Background Knowledge

##### 2.1. Support Vector Machine (SVM)

SVM is a kind of classification algorithm, which is devoted to improving the generalization ability by seeking structural risk minimum of the learning machine. The core idea lies in it is the maximum margin strategy, which can be finally transformed into solving a convex quadratic programming problem. Thanks to the good property, SVM has found its applications in a wide range of fields [19, 20, 23–32].

In a binary classification task, the samples are separated with a hyperplane , where is a -dimensional coefficient vector that is normal to the hyperplane and* b* is the offset from the origin and are data points. The main task of SVM is to get the results of and* b*. In linear case, can be solved by introducing Lagrangian multipliers. The data points on the maximum border are called support vectors. As a result, the solution of takes the following form: , where is the number of SVs and are the labels corresponding samples . After then can be derived from , where are SVs. After and* b* are determined, the linear discriminant function can be given by

In nonlinear cases, a general idea of kernel trick is introduced. And then the decision function can be expressed as follows:

Generally, any positive semidefinite functions that satisfy Mercer’s condition can be used as kernel functions [33], such as the polynomial kernel and the Gaussian kernel .

This section gives a brief description of SVM. For more details, one can refer to [34, 35], which provides a complete description of the SVM theory.

##### 2.2. Binary Grey Wolf Optimization

Grey wolf optimization (GWO) is a metaheuristic algorithm proposed by Mirjalili et al. [9] in 2014. It mimics the social leadership and hunting behavior of grey wolves in nature. In every iteration of GWO, there are three fittest candidate solutions assumed as alpha, beta, and delta to produce that lead the population toward promising regions of the search space. The rest of grey wolves are named as omega and required to assist alpha, beta, and delta to encircle, hunt, and attack prey, that is, to find better solutions.

In order to mathematically simulate the encircling behavior of grey wolves, the following equations are proposed:where indicates the current iteration, , , is the position vector of the prey, is the position vector of a grey wolf, is linearly decreased from 2 to 0, and and are random vectors in .

In order to mathematically simulate the hunting behavior of grey wolves, the following equations are proposed:

In this work, a new binary GWO (IGWO) is proposed for the feature selection task. Figure 1 presents a flowchart of the proposed IGWO. In the IGWO, each grey wolf has a flag vector, whose length is equal to the total number of features in the dataset. When the position of a grey wolf was updated by (6), the following equation is used to discrete the position.where indicates the th position of the th grey wolf.