Jinhua Advanced Research Institute, Jinhua 321000, China.
Comput Intell Neurosci. 2022 Aug 19;2022:2974813. doi: 10.1155/2022/2974813. eCollection 2022.
With the development of teaching evaluation program, colleges and universities have reformed according to the actual situation of the school. With the development of evaluation activities, many universities are eager to establish their own teaching quality evaluation system, so as to pre-evaluate the teaching quality of schools. SVM is one of the most widely used machine learning algorithms that enables efficient statistical learning with a very limited number of samples. Considering the excellent learning performance of SVM, it is very suitable for the teaching quality evaluation system. In this paper, we optimize the existing multiple classification algorithm for binary trees and propose a new method. Learning the popular teaching quality evaluation system in colleges and universities, the binary tree support vector machine classification algorithm, and design comparison experiment, the experimental results show that the evaluation model proposed in this paper has strong generalization ability and higher classification accuracy and better classification efficiency.
随着教学评估计划的发展，高校根据学校的实际情况进行了改革。随着评估活动的发展，许多大学都渴望建立自己的教学质量评估体系，以便预先评估学校的教学质量。SVM 是应用最广泛的机器学习算法之一，它可以在非常有限的样本数量下进行有效的统计学习。考虑到 SVM 的出色学习性能，它非常适合教学质量评估系统。在本文中，我们对现有的二叉树多分类算法进行了优化，并提出了一种新方法。学习高校流行的教学质量评估系统，二叉树支持向量机分类算法，并设计对比实验，实验结果表明，本文提出的评价模型具有较强的泛化能力和更高的分类精度和更好的分类效率。
