A Novel Parameter Refinement Approach to One Class Support Vector Machine

Dat Tran; Wanli Ma; Dharmendra Sharma

doi:10.1007/978-3-642-24958-7_61

A Novel Parameter Refinement Approach to One Class Support Vector Machine

Dat Tran, Wanli Ma, Dharmendra Sharma

Information Systems

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

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 language	English
Title of host publication	International Conference on Neural Information Processing (ICONIP 2011)
Subtitle of host publication	Lecture Notes in Computer Science
Editors	Bao-Liang Lu, Liqing Zhang, James Kwok
Place of Publication	Berlin Heidelberg
Publisher	Springer
Pages	529-536
Number of pages	8
Volume	7063
ISBN (Electronic)	9783642249587
ISBN (Print)	9783642249570
DOIs	https://doi.org/10.1007/978-3-642-24958-7_61
Publication status	Published - 2011
Event	International Conference on Neural Information Processing ICONIP 2011 - Shanghai, Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011

Conference

Conference	International Conference on Neural Information Processing ICONIP 2011
Abbreviated title	ICONIP 2011
Country/Territory	China
City	Shanghai
Period	13/11/11 → 17/11/11

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

Cite this

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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",

doi = "10.1007/978-3-642-24958-7_61",

language = "English",

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editor = "Bao-Liang Lu and Liqing Zhang and James Kwok",

booktitle = "International Conference on Neural Information Processing (ICONIP 2011)",

publisher = "Springer",

address = "Netherlands",

note = "International Conference on Neural Information Processing ICONIP 2011, ICONIP 2011 ; Conference date: 13-11-2011 Through 17-11-2011",

}

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, 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, 2011. p. 529-536.

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

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

CY - Berlin Heidelberg

T2 - International Conference on Neural Information Processing ICONIP 2011

Y2 - 13 November 2011 through 17 November 2011

ER -

A Novel Parameter Refinement Approach to One Class Support Vector Machine

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