Prediction of student poverty levels based on improved linearly separable support vector machine

Longtang Ning; Boonsub Panichakarn; Benxiao Lou; Shixuan Zhou; Xiang Wang; Jianqiu Chen; Yanzhi Pang; Chun Bao; Shiyu Wang; Bote Liu; Guobin Gu

doi:10.1117/12.3039815

4 September 2024 Prediction of student poverty levels based on improved linearly separable support vector machine

Longtang Ning, Boonsub Panichakarn, Benxiao Lou, Shixuan Zhou, Xiang Wang, Jianqiu Chen, Yanzhi Pang, Chun Bao, Shiyu Wang, Bote Liu, Guobin Gu

Author Affiliations +

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132592S (2024) https://doi.org/10.1117/12.3039815
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

The issue of student poverty is not only a core concern in the field of education but also an important topic in socioeconomic development. Identifying and assisting impoverished students is crucial for achieving educational equity and social justice. However, the determination and classification of the poverty levels among students face numerous challenges due to various influencing factors. Therefore, this paper employs a k-means improved linearly separable support vector machine model to classify the levels of poverty. Experimental results show that the classification model performs well, with an accuracy of 97.419%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Longtang Ning, Boonsub Panichakarn, Benxiao Lou, Shixuan Zhou, Xiang Wang, Jianqiu Chen, Yanzhi Pang, Chun Bao, Shiyu Wang, Bote Liu, and Guobin Gu "Prediction of student poverty levels based on improved linearly separable support vector machine", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132592S (4 September 2024); https://doi.org/10.1117/12.3039815

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