Support vector regression with fuzzy target output

Author

Forghani, Y. ; Yazdi, Hadi Sadoghi ; Effati, Sohrab

Author_Institution

Comput. Dept., Azad Univ., Mashhad, Iran

fYear

2011

fDate

13-14 Oct. 2011

Firstpage

54

Lastpage

59

Abstract

In this paper, we incorporate the concept of fuzzy set theory into the support vector regression (SVR). In our proposed method, target outputs of training samples are considered to be fuzzy numbers and then, membership function of actual output (objective hyperplane in high dimensional feature space) is obtained. Two main properties of our proposed method are: (1) membership function of actual output can be obtained without pre-assumption on type of membership function of the bias term and the components of weight vector; (2) the membership function of target output can be each type of fuzzy number.

Keywords

fuzzy set theory; regression analysis; support vector machines; SVR; fuzzy numbers; fuzzy set theory; fuzzy target output; high dimensional feature space; membership function; objective hyperplane; support vector regression; training sample target output; Computers; Mathematical model; Support vector machines; Training; Training data; Upper bound; Vectors; Fuzzy bias; Fuzzy target output; Fuzzy weight; Support vector regression (SVR);

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Knowledge Engineering (ICCKE), 2011 1st International eConference on

Conference_Location

Mashhad

Print_ISBN

978-1-4673-5712-8

Type

conf

DOI

10.1109/ICCKE.2011.6413324

Filename

6413324