DocumentCode
2850265
Title
A method for missing data interpolation by SVR
Author
Wang, Xingheng ; Deng, Xue ; Liu, Yao ; Cao, Jun ; Gao, Shi
Author_Institution
Sch. of Inf. Sci. & Technol., East China Normal Univ., Shanghai, China
fYear
2012
fDate
24-27 June 2012
Firstpage
132
Lastpage
135
Abstract
In this paper, an approach for interpolating the missing data by support vector regression (SVR) machine is proposed. First, the samples where some features are missing are separated from the original samples. Then the remaining samples are trained by SVR, where the feature values corresponding to the missing features are treated as the labels. Finally, the obtained hyper-surface is used to predict the missing features. Experimental results show the considerable effectiveness of the proposed method.
Keywords
data analysis; interpolation; regression analysis; support vector machines; SVR; feature values; hyper-surface; missing data interpolation; missing features; support vector regression machine; Interpolation; interpolating; missing data; support vector regression;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical & Electronics Engineering (EEESYM), 2012 IEEE Symposium on
Conference_Location
Kuala Lumpur
Print_ISBN
978-1-4673-2363-5
Type
conf
DOI
10.1109/EEESym.2012.6258606
Filename
6258606
Link To Document