Fuzzy multi-sphere support vector data description

Trung Le; Dat Tran; Wanli Ma; Dharmendra Sharma

doi:10.1109/FUZZ-IEEE.2012.6251336

Fuzzy multi-sphere support vector data description

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
ISBN (Electronic)	9781467315050
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2012.6251336
Publication status	Published - 2012
Externally published	Yes
Event	IEEE International Conference on Fuzzy Systems 2012 - Brisbane, Australia Duration: 10 Jun 2012 → 15 Jun 2012 Conference number: 21st https://ieeexplore.ieee.org/xpl/conhome/6241469/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Fuzzy Systems 2012
Abbreviated title	FUZZ-IEEE 2012
Country/Territory	Australia
City	Brisbane
Period	10/06/12 → 15/06/12
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6241469/proceeding (Proceedings)

Keywords

fuzzy model
Novelty detection
one-class classification
support vector data description

Access to Document

10.1109/FUZZ-IEEE.2012.6251336

Cite this

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title = "Fuzzy multi-sphere support vector data description",

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 = "fuzzy model, Novelty detection, one-class classification, support vector data description",

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year = "2012",

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note = "IEEE International Conference on Fuzzy Systems 2012, FUZZ-IEEE 2012 ; Conference date: 10-06-2012 Through 15-06-2012",

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AU - Le, Trung

AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

N1 - Conference code: 21st

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - fuzzy model

KW - Novelty detection

KW - one-class classification

KW - support vector data description

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U2 - 10.1109/FUZZ-IEEE.2012.6251336

DO - 10.1109/FUZZ-IEEE.2012.6251336

M3 - Conference Paper

AN - SCOPUS:84867616343

SN - 9781467315067

SN - 9781467315074

BT - 2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012

T2 - IEEE International Conference on Fuzzy Systems 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

Fuzzy multi-sphere support vector data description

Abstract

Conference

Keywords

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