Manuscript Number : IJSRSET196431
Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm
Authors(1) :-Qingmi Yang
The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.
Qingmi Yang
Support Vector Machine; Principal Component Analysis; Particle Swarm Optimization; parameter optimization
Publication Details
Published in :
Volume 6 | Issue 4 | July-August 2019 Article Preview
School of Communication Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
Date of Publication :
2019-08-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
325-330
Manuscript Number :
IJSRSET196431
Publisher : Technoscience Academy
Journal URL :
https://ijsrset.com/IJSRSET196431