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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Application of Optimized SVM in Sample Classification

Author 1: Xuemei Yao

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130666

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 6, 2022.

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Abstract: Support vector machines (SVM) have unique advantages in solving problems with small samples, nonlinearity and high dimension. It has a relatively complete theory and has been widely used in various fields. The classification accuracy and generalization ability of SVMs are determined by the selected parameters, for which there is no solid theoretical guidance. To address this parameter optimization problem, we applied random selection, genetic algorithms (GA), particle swarm optimization (PSO) and K-fold cross validation (k-CV) method to optimize the parameters of SVMs. Taking the classification accuracy, mean squared error and squared correlation coefficient as the goal, the K-fold cross validation method is chosen as the best way to optimize SVM parameters. In order to further verify the best performance of the SVM whose parameters are optimized by the K-fold cross validation method, the back propagation neural network and decision tree are used as the contrast models. The experimental results show that the SVM-cross validation method has the highest classification accuracy in SVM parameter selection, which lead to SVM classifiers that outperform both BP neural networks and decision tree method.

Keywords: Support vector machine; parameter optimization; K-fold cross validation; sample classification

Xuemei Yao, “Application of Optimized SVM in Sample Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130666

@article{Yao2022,
title = {Application of Optimized SVM in Sample Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130666},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130666},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Xuemei Yao}
}


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