An Early Warning Model for Student Status Based on Genetic Algorithm-Optimized Radial Basis Kernel Support Vector Machine
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초록
A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of theirstatus. This model improves the predictive effect of support vector machines. The genetic optimizationalgorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, andgamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain theoptimal solution. The experimental model was trained on open-source datasets and validated throughcomparisons with random forest, backpropagation neural network, and GA-SVM models. The test results showthat the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtainhigher accuracy rates when used for early warning in university learning.
Abstract
A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of theirstatus. This model improves the predictive effect of support vector machines. The genetic optimizationalgorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, andgamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain theoptimal solution. The experimental model was trained on open-source datasets and validated throughcomparisons with random forest, backpropagation neural network, and GA-SVM models. The test results showthat the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtainhigher accuracy rates when used for early warning in university learning.
참고문헌 (10)
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