DocumentCode
2387787
Title
Feature selection based on the feature space class separability criterion
Author
Liang, Siyang ; Li, Ming ; Liang, Guanhui ; Gao, Qing
Author_Institution
Beijing Inst. of Technol., Beijing, China
fYear
2012
fDate
19-20 May 2012
Firstpage
711
Lastpage
713
Abstract
Aimed at the imbalance of training samples, isolated points, and the importance degree of class samples of different three questions, this paper put forward a improvement weighted support vector machine (SVM), and give the method of determine the integrated weights, the simulation results show the effectiveness of the method.
Keywords
feature extraction; pattern classification; support vector machines; training; SVM; feature selection; feature space class separability criterion; integrated weights method; isolated points; support vector machine; training samples; Accuracy; Optimization; Simulation; Support vector machine classification; Training; Vectors; class separability criterion; feature space; joint optimization; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4673-0198-5
Type
conf
DOI
10.1109/ICSAI.2012.6223093
Filename
6223093
Link To Document