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.

Authors and Affiliations

Qingmi Yang
School of Communication Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China

Support Vector Machine; Principal Component Analysis; Particle Swarm Optimization; parameter optimization

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Publication Details

Published in : Volume 6 | Issue 4 | July-August 2019
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

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Qingmi Yang, " Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 4, pp.325-330, July-August-2019. Available at doi : https://doi.org/10.32628/IJSRSET196431      Citation Detection and Elimination     |     
Journal URL : https://ijsrset.com/IJSRSET196431

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