An Early Warning Model for Student Status Based on Genetic Algorithm-Optimized Radial Basis Kernel Support Vector Machine


Hui Li, Qixuan Huang, Chao Wang, Journal of Information Processing Systems Vol. 20, No. 2, pp. 263-272, Apr. 2024  

https://doi.org/10.3745/JIPS.02.0213
Keywords: Early Warning Model, Genetic Algorithm Optimization, Radial Basis Kernel, Support Vector Machine
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Abstract

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.


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Cite this article
[APA Style]
Li, H., Huang, Q., & Wang, C. (2024). An Early Warning Model for Student Status Based on Genetic Algorithm-Optimized Radial Basis Kernel Support Vector Machine. Journal of Information Processing Systems, 20(2), 263-272. DOI: 10.3745/JIPS.02.0213.

[IEEE Style]
H. Li, Q. Huang, C. Wang, "An Early Warning Model for Student Status Based on Genetic Algorithm-Optimized Radial Basis Kernel Support Vector Machine," Journal of Information Processing Systems, vol. 20, no. 2, pp. 263-272, 2024. DOI: 10.3745/JIPS.02.0213.

[ACM Style]
Hui Li, Qixuan Huang, and Chao Wang. 2024. An Early Warning Model for Student Status Based on Genetic Algorithm-Optimized Radial Basis Kernel Support Vector Machine. Journal of Information Processing Systems, 20, 2, (2024), 263-272. DOI: 10.3745/JIPS.02.0213.