HRRP Classification by Using Improved SVM Decision Tree

Author

Wang, Xiaodan ; Wu, Chongming ; Qi, Yina ; Bai, Dongying

Author_Institution

Dept. of Comput. Eng., Air Force Eng. Univ., ShaanXi

Volume

2

fYear

0

fDate

0-0 0

Firstpage

10096

Lastpage

10100

Abstract

Support vector machine (SVM) has been used in high resolution range profile (HRRP) classification for its good generalization ability for the pattern classification problem with high feature dimension and small training set. In order to perform multi-class classification, decision-tree-based SVM was studied, the structure and the classification performance of the SVM decision tree was analyzed. A separability measure which based on the distribution of the training samples was defined, the defined separability measure was applied into the formation of the decision tree, and an improved algorithm for SVM decision tree was proposed. The scheme of using the improved algorithm for SVM decision tree to classify HRRP was given. Experiments using the range profile datasets prove the effectiveness of our scheme

Keywords

decision trees; pattern classification; support vector machines; SVM decision tree; high resolution range profile classification; multiclass classification; pattern classification; support vector machine; Classification tree analysis; Decision trees; Electronic mail; Intelligent control; Military computing; Missiles; Pattern classification; Performance analysis; Support vector machine classification; Support vector machines; decision tree; high resolution range profile; separability measure; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713975

Filename

1713975