A SVM multi-classification method based on fuzzy weighted SVDD

Author

Xiao-Yun, Shi ; Tie, Yu ; Quan, Chen

Author_Institution

Tianjin Univ. of Technol., Tianjin, China

Volume

2

fYear

2010

fDate

3-5 Oct. 2010

Abstract

Aiming at sensitivity of noise on WSVDD and circumstance of can not be separated in multi-classification problem, the paper presented a multi-classification method based on fuzzy weighted support vector description algorithm. Inspired by weighed SVDD, the method assigned weight to each training sample to build super-ball, while its weight does not take into account the effect of characteristics of sample data itself and noise. Noise fuzzy kernel clustering method was used to determine membership degree of samples and fuzzy weighted SVDD model was built. The multi-classification algorithm and simple classification rules were also provided. The example proves that the method can effectively reduce the effect of noise on classifier, and it can also achieve relatively better training accuracy.

Keywords

fuzzy set theory; pattern classification; pattern clustering; support vector machines; SVM multiclassification method; fuzzy weighted SVDD; noise fuzzy kernel clustering method; noise sensitivity; super-ball; support vector description algorithm; Classification algorithms; Clustering algorithms; Kernel; Noise; Optimization; Support vector machines; Training; fuzzy clustering; multi-classification method; support vector data description;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Technology and Engineering (ICSTE), 2010 2nd International Conference on

Conference_Location

San Juan, PR

Print_ISBN

978-1-4244-8667-0

Electronic_ISBN

978-1-4244-8666-3

Type

conf

DOI

10.1109/ICSTE.2010.5608761

Filename

5608761