A Novel Parameter Refinement Approach to One Class Support Vector Machine

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

One-Class Support Vector Machine employs a grid parameter selection process to discover the best parameters for a given data set. It is assumed that two separate trade-off parameters are assigned to normal and abnormal data samples, respectively. However, this assumption is not always true because data samples have different contributions to the construction of hypersphere or hyperplane decision boundary. In this paper, we introduce a new iterative learning process that is carried out right after the grid parameter selection process to refine the trade-off parameter value for each sample. In this learning process, a weight is assigned to each sample to represent the contribution of that sample and is iteratively refined. Experimental results performed on a number of data sets show a better performance for the proposed approach
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2011)
Subtitle of host publicationLecture Notes in Computer Science
EditorsBao-Liang Lu, Liqing Zhang, James Kwok
Place of PublicationBerlin Heidelberg
PublisherSpringer Verlag
Pages529-536
Number of pages8
Volume7063
ISBN (Electronic)9783642249587
ISBN (Print)9783642249570
DOIs
Publication statusPublished - 2011
EventInternational Conference on Neural Information Processing ICONIP 2011 - Shanghai, Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Conference

ConferenceInternational Conference on Neural Information Processing ICONIP 2011
Abbreviated titleICONIP 2011
CountryChina
CityShanghai
Period13/11/1117/11/11

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Support vector machines

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Tran, D., Ma, W., & Sharma, D. (2011). A Novel Parameter Refinement Approach to One Class Support Vector Machine. In B-L. Lu, L. Zhang, & J. Kwok (Eds.), International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science (Vol. 7063, pp. 529-536). Berlin Heidelberg: Springer Verlag. https://doi.org/10.1007/978-3-642-24958-7_61
Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / A Novel Parameter Refinement Approach to One Class Support Vector Machine. International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. editor / Bao-Liang Lu ; Liqing Zhang ; James Kwok. Vol. 7063 Berlin Heidelberg : Springer Verlag, 2011. pp. 529-536
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title = "A Novel Parameter Refinement Approach to One Class Support Vector Machine",
abstract = "One-Class Support Vector Machine employs a grid parameter selection process to discover the best parameters for a given data set. It is assumed that two separate trade-off parameters are assigned to normal and abnormal data samples, respectively. However, this assumption is not always true because data samples have different contributions to the construction of hypersphere or hyperplane decision boundary. In this paper, we introduce a new iterative learning process that is carried out right after the grid parameter selection process to refine the trade-off parameter value for each sample. In this learning process, a weight is assigned to each sample to represent the contribution of that sample and is iteratively refined. Experimental results performed on a number of data sets show a better performance for the proposed approach",
keywords = "Machine Learning, Support Vector Machine, Novelty Detection",
author = "Dat Tran and Wanli Ma and Dharmendra Sharma",
year = "2011",
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language = "English",
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volume = "7063",
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editor = "Bao-Liang Lu and Liqing Zhang and James Kwok",
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publisher = "Springer Verlag",
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}

Tran, D, Ma, W & Sharma, D 2011, A Novel Parameter Refinement Approach to One Class Support Vector Machine. in B-L Lu, L Zhang & J Kwok (eds), International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. vol. 7063, Springer Verlag, Berlin Heidelberg, pp. 529-536, International Conference on Neural Information Processing ICONIP 2011, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24958-7_61

A Novel Parameter Refinement Approach to One Class Support Vector Machine. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. ed. / Bao-Liang Lu; Liqing Zhang; James Kwok. Vol. 7063 Berlin Heidelberg : Springer Verlag, 2011. p. 529-536.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - A Novel Parameter Refinement Approach to One Class Support Vector Machine

AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

PY - 2011

Y1 - 2011

N2 - One-Class Support Vector Machine employs a grid parameter selection process to discover the best parameters for a given data set. It is assumed that two separate trade-off parameters are assigned to normal and abnormal data samples, respectively. However, this assumption is not always true because data samples have different contributions to the construction of hypersphere or hyperplane decision boundary. In this paper, we introduce a new iterative learning process that is carried out right after the grid parameter selection process to refine the trade-off parameter value for each sample. In this learning process, a weight is assigned to each sample to represent the contribution of that sample and is iteratively refined. Experimental results performed on a number of data sets show a better performance for the proposed approach

AB - One-Class Support Vector Machine employs a grid parameter selection process to discover the best parameters for a given data set. It is assumed that two separate trade-off parameters are assigned to normal and abnormal data samples, respectively. However, this assumption is not always true because data samples have different contributions to the construction of hypersphere or hyperplane decision boundary. In this paper, we introduce a new iterative learning process that is carried out right after the grid parameter selection process to refine the trade-off parameter value for each sample. In this learning process, a weight is assigned to each sample to represent the contribution of that sample and is iteratively refined. Experimental results performed on a number of data sets show a better performance for the proposed approach

KW - Machine Learning

KW - Support Vector Machine

KW - Novelty Detection

U2 - 10.1007/978-3-642-24958-7_61

DO - 10.1007/978-3-642-24958-7_61

M3 - Conference contribution

SN - 9783642249570

VL - 7063

SP - 529

EP - 536

BT - International Conference on Neural Information Processing (ICONIP 2011)

A2 - Lu, Bao-Liang

A2 - Zhang, Liqing

A2 - Kwok, James

PB - Springer Verlag

CY - Berlin Heidelberg

ER -

Tran D, Ma W, Sharma D. A Novel Parameter Refinement Approach to One Class Support Vector Machine. In Lu B-L, Zhang L, Kwok J, editors, International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science. Vol. 7063. Berlin Heidelberg: Springer Verlag. 2011. p. 529-536 https://doi.org/10.1007/978-3-642-24958-7_61