DocumentCode
2550792
Title
A fuzzy rough support vector regression machine
Author
Xue, Zhenxia ; Liu, Wanli
Author_Institution
Sch. of Math. & Stat., Henan Univ. of Sci. & Technol., Luoyang, China
fYear
2012
fDate
29-31 May 2012
Firstpage
840
Lastpage
844
Abstract
A fuzzy rough support vector regression (FRSVM) is proposed to deal with the overfitting problem caused by outliers in v - support vector regression (v - SVR). Based on rough set theory, the training data points are divided into three regions, i.e., positive region, boundary region and negative region. A fuzzy membership function is also applied to the training data points. Experimental results on benchmark datasets confirm the validity and feasibility of our proposed algorithm.
Keywords
fuzzy set theory; regression analysis; rough set theory; support vector machines; fuzzy membership function; fuzzy rough support vector regression machine; outliers; overfitting problem; rough set theory; training data points; Educational institutions; Electron tubes; Set theory; Support vector machines; Training; Upper bound; Vectors; outlier; overfitting; support vector regression;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location
Sichuan
Print_ISBN
978-1-4673-0025-4
Type
conf
DOI
10.1109/FSKD.2012.6234232
Filename
6234232
Link To Document