Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Simultaneous Feature Selection and LS-SVM Parameters Optimization Algorithm Based on PSO

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Quan-Zhu Yao ; Sch. of Comput. Sci. & Eng., Xi''an Univ. of Technol., Xi''an, China ; Jie Cai ; Jiu-Long Zhang

For the feature selection and parameter optimization of LS-SVM, propose a At first, a population of particles (feature subsets) was randomly generated, then the features and parameters are optimized by PSO algorithm. The experiments on the UCI database indicate that the proposed method can efficiently find the suitable feature subsets and LS-SVM parameters. Also, comparison are made against GALS-SVM and LS-SVM; and the results show that the proposed PSOLS-SVM outperform the others in classification performance.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:5 )

Date of Conference:

March 31 2009-April 2 2009