A SA-based feature selection and parameter optimization approach for support vector machine

S. W. Lin*, T. Y. Tseng, S. C. Chen, J. F. Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Support Vector Machine (SVM) is a new technique for pattern classification, and is used in many applications. Kernel parameter setting in the SVM training process, along with feature selection, will significantly impact the classification accuracy. The objective of this study is to simultaneously optimize parameters while finding a subset of features without degrading SVM classification accuracy. A simulated annealing (SA) approach for feature selection and parameters optimization was developed. Several UCI datasets are tested using the SA-based approach and the grid algorithm which is a traditional method of performing parameter searching. Compared with the grid algorithm, the proposed SA-based approach significantly improves the classification accuracy rate and requires fewer input features for the SVM.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
Pages3144
Number of pages1
DOIs
StatePublished - 2007
Externally publishedYes
Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan
Duration: 08 10 200611 10 2006

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume4
ISSN (Print)1062-922X

Conference

Conference2006 IEEE International Conference on Systems, Man and Cybernetics
Country/TerritoryTaiwan
CityTaipei
Period08/10/0611/10/06

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