Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)

Trung Minh LE; Dat TRAN; Wanli MA

doi:10.1007/978-3-642-37456-2_48

Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)

Trung Minh LE, Dat TRAN, Wanli MA

Information Systems

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

8 Citations (Scopus)

Abstract

Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented

Original language	English
Title of host publication	Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)
Editors	Jian Pei, Vincent S Tseny, Longbird Cao, Hiroshi Motoda, Guandong Xu
Place of Publication	Berlin
Publisher	Springer
Pages	570-581
Number of pages	12
Volume	7819
ISBN (Print)	9783642374524
DOIs	https://doi.org/10.1007/978-3-642-37456-2_48
Publication status	Published - 2013
Event	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining - Gold Coast, Gold Coast, Australia Duration: 14 Apr 2013 → 17 Apr 2013

Conference

Conference	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Country/Territory	Australia
City	Gold Coast
Period	14/04/13 → 17/04/13

Access to Document

10.1007/978-3-642-37456-2_48

Cite this

LE, T. M., TRAN, D., & MA, W. (2013). Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). In J. Pei, V. S. Tseny, L. Cao, H. Motoda, & G. Xu (Eds.), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013) (Vol. 7819, pp. 570-581). Springer. https://doi.org/10.1007/978-3-642-37456-2_48

LE, Trung Minh ; TRAN, Dat ; MA, Wanli. / Fuzzy Multi-Sphere Support Vector Data Description : Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). editor / Jian Pei ; Vincent S Tseny ; Longbird Cao ; Hiroshi Motoda ; Guandong Xu. Vol. 7819 Berlin : Springer, 2013. pp. 570-581

@inproceedings{444ad92fc9ab455aac69e284db40c076,

title = "Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)",

abstract = "Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented",

keywords = "Support Vector Data Description, Kernel Method, EEG",

author = "LE, {Trung Minh} and Dat TRAN and Wanli MA",

year = "2013",

doi = "10.1007/978-3-642-37456-2_48",

language = "English",

isbn = "9783642374524",

volume = "7819",

pages = "570--581",

editor = "Jian Pei and Tseny, {Vincent S} and Longbird Cao and Hiroshi Motoda and Guandong Xu",

booktitle = "Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)",

publisher = "Springer",

address = "Netherlands",

note = "17th Pacific-Asia Conference on Knowledge Discovery and Data Mining ; Conference date: 14-04-2013 Through 17-04-2013",

}

LE, TM, TRAN, D & MA, W 2013, Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). in J Pei, VS Tseny, L Cao, H Motoda & G Xu (eds), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). vol. 7819, Springer, Berlin, pp. 570-581, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, Queensland, Australia, 14/04/13. https://doi.org/10.1007/978-3-642-37456-2_48

Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). / LE, Trung Minh; TRAN, Dat ; MA, Wanli.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). ed. / Jian Pei; Vincent S Tseny; Longbird Cao; Hiroshi Motoda; Guandong Xu. Vol. 7819 Berlin: Springer, 2013. p. 570-581.

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

TY - GEN

T1 - Fuzzy Multi-Sphere Support Vector Data Description

T2 - 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining

AU - LE, Trung Minh

AU - TRAN, Dat

AU - MA, Wanli

PY - 2013

Y1 - 2013

N2 - Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented

AB - Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented

KW - Support Vector Data Description

KW - Kernel Method

KW - EEG

U2 - 10.1007/978-3-642-37456-2_48

DO - 10.1007/978-3-642-37456-2_48

M3 - Conference contribution

SN - 9783642374524

VL - 7819

SP - 570

EP - 581

BT - Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)

A2 - Pei, Jian

A2 - Tseny, Vincent S

A2 - Cao, Longbird

A2 - Motoda, Hiroshi

A2 - Xu, Guandong

PB - Springer

CY - Berlin

Y2 - 14 April 2013 through 17 April 2013

ER -

Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)

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