Fuzzy Multi-sphere Support Vector Data Description

Author

Le, Trung ; Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra

Author_Institution

Fac. of Inf. Sci. & Eng., Univ. of Canberra, Canberra, ACT, Australia

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

5

Abstract

Multi-sphere Support Vector Data Description (MS-SVDD) has been proposed in our previous work. MS-SVDD aims to build a set of spherically shaped boundaries that provide a better data description to the normal dataset and an iterative learning algorithm that determines the set of spherically shaped boundaries. MS-SVDD could improve classification rate for one-class classification problems comparing with SVDD. However MS-SVDD requires a small abnormal data set to build the spherically shaped boundaries for the normal data set. In this paper, we propose a new fuzzy MS-SVDD that can be used when only the normal data set is available. Experimental results on 14 well-known datasets and a comparison between fuzzy MS-SVDD and SVDD are also presented.

Keywords

data handling; fuzzy set theory; iterative methods; learning (artificial intelligence); support vector machines; Fuzzy multisphere support vector data description; MS-SVDD; data description; iterative learning algorithm; spherically shaped boundaries; Data models; Iterative methods; Kernel; Machine learning; Optimization; Support vector machines; Vectors; Novelty detection; fuzzy model; one-class classification; support vector data description;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on

Conference_Location

Brisbane, QLD

ISSN

1098-7584

Print_ISBN

978-1-4673-1507-4

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZ-IEEE.2012.6251336

Filename

6251336