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: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-5
Number of pages	5
ISBN (Print)	9781467315050
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2012.6251336
Publication status	Published - 15 Jun 2012
Event	2012 IEEE International Conference on Fuzzy Systems - Brisbane, QLD, Australia Duration: 10 Jun 2012 → 15 Jun 2012

Conference

Conference	2012 IEEE International Conference on Fuzzy Systems
Period	10/06/12 → 15/06/12

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10.1109/FUZZ-IEEE.2012.6251336

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6251336

Cite this

@inproceedings{9071d05035644802825f84cdf2761124,

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 = "Support vector machines, Optimization, Machine learning, Data models, Vectors, Iterative methods, Kernel",

author = "Trung Le and Dat Tran and Wanli Ma and Dharmendra Sharma",

year = "2012",

month = jun,

day = "15",

doi = "10.1109/FUZZ-IEEE.2012.6251336",

language = "English",

isbn = "9781467315050",

pages = "1--5",

booktitle = "2012 IEEE International Conference on Fuzzy Systems",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

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

}

TY - GEN

T1 - Fuzzy Multi-sphere Support Vector Data Description

AU - Le, Trung

AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

PY - 2012/6/15

Y1 - 2012/6/15

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 - Support vector machines

KW - Optimization

KW - Machine learning

KW - Data models

KW - Vectors

KW - Iterative methods

KW - Kernel

UR - https://ieeexplore.ieee.org/document/6251336/

U2 - 10.1109/FUZZ-IEEE.2012.6251336

DO - 10.1109/FUZZ-IEEE.2012.6251336

M3 - Conference contribution

SN - 9781467315050

SP - 1

EP - 5

BT - 2012 IEEE International Conference on Fuzzy Systems

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 2012 IEEE International Conference on Fuzzy Systems

Y2 - 10 June 2012 through 15 June 2012

ER -

Fuzzy Multi-sphere Support Vector Data Description

Abstract

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