DocumentCode
3431438
Title
Research on grain information classification based on SVM decision tree
Author
Geng, Ruihuan ; Zhang, Dexian ; Chai, Jiajia
Author_Institution
College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China
fYear
2012
fDate
11-13 Aug. 2012
Firstpage
138
Lastpage
141
Abstract
The defections of traditional support vector machine (for short SVM) are analyzed in the paper. According to the characteristics of grain information on the web, a multi-class classification method based on SVM decision tree (for short SVM-DT) is presented for grain information classification. Experiments prove that F1-Measure values for SVM-DT algorithm is superior to the traditional SVM algorithm. It is more suitable for application to grain information classification system.
Keywords
Frequency measurement; Noise; Support vector machines; Grain information; SVM Binary tree; Web text mining;
fLanguage
English
Publisher
ieee
Conference_Titel
Granular Computing (GrC), 2012 IEEE International Conference on
Conference_Location
Hangzhou, China
Print_ISBN
978-1-4673-2310-9
Type
conf
DOI
10.1109/GrC.2012.6468622
Filename
6468622
Link To Document