Application of Optimized SVM in Sample Classification

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.

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