Prior knowledge-based fuzzy Support Vector Regression

Author

Ling Wang ; Zhi Chun Mu ; Hui Guo

Author_Institution

Dept. of Autom., Univ. of Sci. & Technol. Beijing, Beijing

fYear

2008

fDate

1-6 June 2008

Firstpage

392

Lastpage

395

Abstract

A new method was proposed for incorporating prior knowledge in the form of fuzzy knowledge sets into Support Vector Machine for regression problem. The prior knowledge of Fuzzy IF-THEN rules can be transformed into fuzzy information to generate fuzzy kernel, based on which FSVR (Fuzzy Support Vector Regression) is introduced. The merit of FSVR is that it can incorporate with prior knowledge represented by fuzzy IF-THEN rules to improve the performance of the conventional SVR in incomplete numeral dataset for training. The simulation results are feasible.

Keywords

fuzzy set theory; knowledge representation; learning (artificial intelligence); regression analysis; support vector machines; fuzzy if-then rule; fuzzy kernel; fuzzy knowledge set; learning theory; prior knowledge representation; regression analysis; support vector machine; Equations; Fuzzy set theory; Fuzzy sets; Kernel; Multilayer perceptrons; Polynomials; Risk management; Support vector machine classification; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on

Conference_Location

Hong Kong

ISSN

1098-7584

Print_ISBN

978-1-4244-1818-3

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZY.2008.4630397

Filename

4630397