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Research on the Course Discrimination Based on Gray Wolf Algorithm and Support Vector Machine

机译：基于灰狼算法和支持向量机的课程区分研究。

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摘要

The division of the test paper can reflect the quality of examination paper, but it is difficult to find some decisive courses in dozens of courses. In order to find out the curriculum that decides the role of different levels of students, the concept of course discrimination is proposed, which focuses on the value of course discrimination, the classification method and the proportion of special courses selected at this time. For the sake of calculating the value of course discrimination, a method of wrapping feature selection is proposed, which combines gray wolf algorithm and support vector machine (GWO + SVM). So as to obtain the courses distinction of three specialties, eight algorithms were tested. The experimental results show that GWO+SVM is more suitable for calculating the courses distinction than other seven algorithms.
机译：试卷的划分可以反映试卷的质量，但是很难在几十门课程中找到一些决定性的课程。为了找出决定不同层次学生角色的课程，提出了课程歧视的概念，重点关注课程歧视的价值，分类方法和目前所选择的特殊课程的比例。为了计算过程判别的价值，提出了一种融合特征选择的方法，该方法结合了灰狼算法和支持向量机（GWO + SVM）。为了获得三个专业的课程区分，测试了八种算法。实验结果表明，与其他七种算法相比，GWO + SVM更适合计算课程差异。

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