SVM-OD: SVM method to detect outliers

Wang, J; Zhang, C; Wu, X; Qi, H

SVM-OD: SVM method to detect outliers

Wang, J Zhang, C

Wu, X Qi, H

Permalink

Publication Type:: Conference Proceeding
Citation:: Studies in Computational Intelligence, 2006, 9 pp. 129 - 141
Issue Date:: 2006-12-01

Closed Access

	Filename	Description	Size
	Wang-J.chp%3A10.1007%2F11539827_7.pdf	Published version	355.58 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Qi, H	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	Studies in Computational Intelligence, 2006, 9 pp. 129 - 141	en_US
dc.identifier.isbn	9783540283157	en_US
dc.identifier.issn	1860-949X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33946
dc.description.abstract	Outlier detection is an important task in data mining because outliers can be either useful knowledge or noise. Many statistical methods have been applied to detect outliers, but they usually assume a given distribution of data and it is difficult to deal with high dimensional data. The Statistical Learning Theory (SLT) established by Vapnik et al. provides a new way to overcome these drawbacks. According to SLT Schölkopf et al. proposed a ν-Support Vector Machine (ν-SVM) andapplied it to detect outliers. However, it is still difficult for data mining users to decide onekey parameter in ν-SVM. This paper proposes a new SVM method to detect outliers, SVM-OD, which can avoid this parameter. We provide the theoretical analysis based on SLT as well as experiments to verify the effectiveness of our method. Moreover, an experiment on synthetic data shows that SVM-OD can detect some local outliers near the cluster with some distribution while ν-SVM cannot dothat. © 2006 Springer-Verlag Berlin/Heidelberg.	en_US
dc.relation.ispartof	Studies in Computational Intelligence	en_US
dc.relation.isbasedon	10.1007/11539827-7	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	SVM-OD: SVM method to detect outliers	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Melbourne, FL
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

Outlier detection is an important task in data mining because outliers can be either useful knowledge or noise. Many statistical methods have been applied to detect outliers, but they usually assume a given distribution of data and it is difficult to deal with high dimensional data. The Statistical Learning Theory (SLT) established by Vapnik et al. provides a new way to overcome these drawbacks. According to SLT Schölkopf et al. proposed a ν-Support Vector Machine (ν-SVM) andapplied it to detect outliers. However, it is still difficult for data mining users to decide onekey parameter in ν-SVM. This paper proposes a new SVM method to detect outliers, SVM-OD, which can avoid this parameter. We provide the theoretical analysis based on SLT as well as experiments to verify the effectiveness of our method. Moreover, an experiment on synthetic data shows that SVM-OD can detect some local outliers near the cluster with some distribution while ν-SVM cannot dothat. © 2006 Springer-Verlag Berlin/Heidelberg.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/33946