Open AccessStudies on the evaluation of college classroom teaching quality based on SVM multiclass classification algorithm
Author(s) -
Xinghui Wu,
Yiming Zhou,
Haihua Xing
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1735/1/012011
Subject(s) - support vector machine , computer science , class (philosophy) , quality (philosophy) , multiclass classification , euclidean distance , algorithm , artificial intelligence , process (computing) , sample (material) , machine learning , statistical classification , philosophy , chemistry , epistemology , chromatography , operating system
In order to improve the evaluation accuracy and efficiency of the quality of classroom teaching in colleges and universities, combined with the teaching process and evaluation system of colleges and universities, a multi-class classification algorithm is proposed, namely, the Distance binary tree support vector machine based on Euclidean distance (DBT-SVM) algorithm, and the algorithm is used to predict the quality evaluation of classroom teaching in colleges and universities. The algorithm uses the Euclidean distance of the center of the two nearest sample classes to influence the classification, so that the first isolated classes can be separated first. Experimentally, the algorithm can improve the efficiency and accuracy of classification and solve the problem of multi-class classification.