Research on Combination Optimization of Parameters and Character Choice for SVM Based on Simulated Annealing and Improved QPSO

Abstract:

In order to improve the classification accuracy of SVM, combination optimization of parameters and character choice for SVM was proposed. Improved QPSO algorithm was researched on in this paper. At the same time, simulated annealing and improved QPSO algorithm was adopted to train SVM in this paper in order to improve its training speed and diagnosis precision. At last the method mentioned above was applied to train and validate SVM using UCI data, and the result showed that this method was very good.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 3384-3387
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.3384
• Online since: May 2014
• Authors: Wan Ling Li*, Yao Zhou Liu, Da Quan Deng
• Keywords: Character Choice, Combination Optimization, IQPSO, QPSO, Simulated Annealing (SA), SVM

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