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22 February 2023 Research on the identification method of poor students based on SVM and decision tree algorithm
Shuqin Hao, Yinming Zhang, Yun Qing
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Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 1258725 (2023) https://doi.org/10.1117/12.2667195
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China
Abstract
In recent years, the expansion of colleges and universities has led to a sharp rise in the number of students, and the number of poor students has also increased, which has greatly increased the difficulty and workload of financial aid for poor students. In order to improve the accuracy and efficiency of poor student identification, there is an urgent need to adopt digital and intelligent measures to assist poor student identification. In this paper, we propose a method to identify needy students using SVM and decision tree algorithm. Firstly, students' campus card consumption information is preprocessed to obtain the consumption poverty index of each student by SVM classification model. Then the decision tree algorithm is used to derive the student's family poverty index based on the student's family information. Finally, the comprehensive poverty index is calculated by weighted summation. The experimental results show that the proposed method realizes the statistics and analysis of students' consumption and family information, and it can identify poor students more accurately, which effectively improves the efficiency and accuracy of poor students' identification.
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Shuqin Hao, Yinming Zhang, and Yun Qing "Research on the identification method of poor students based on SVM and decision tree algorithm", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 1258725 (22 February 2023); https://doi.org/10.1117/12.2667195
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KEYWORDS
Decision trees
Universities
Data modeling
Statistical analysis
Support vector machines
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