Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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An Early Warning Model for Student Status Based on Genetic Algorithm-Optimized Radial Basis Kernel Support Vector Machine

  • Hui Li (School of Information Engineering, Handan University) ;
  • Qixuan Huang (School of Information Engineering, Handan University) ;
  • Chao Wang (School of Software, Handan University)
  • Received : 2023.07.11
  • Accepted : 2023.12.09
  • Published : 2024.04.30
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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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Acknowledgement

This paper is funded by Science and Technology Project of Hebei Education Department (No. QN2021405) and Handan Science and Technology Research and Development Plan Project (No. 21422021173 and 21422031170) and Research Fund of Handan University (No. XZ2021202).

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