Adaptive online one-class support vector machines with applications in structural health monitoring

Anaissi, A; Khoa, NLD; Rakotoarivelo, T; Alamdari, MM; Wang, Y

Adaptive online one-class support vector machines with applications in structural health monitoring

Anaissi, A Khoa, NLD Rakotoarivelo, T Alamdari, MM Wang, Y

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Intelligent Systems and Technology, 2018, 9, (6)
Issue Date:: 2018-11-01

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Field	Value	Language
dc.contributor.author	Anaissi, A
dc.contributor.author	Khoa, NLD
dc.contributor.author	Rakotoarivelo, T
dc.contributor.author	Alamdari, MM
dc.contributor.author	Wang, Y https://orcid.org/0000-0002-6815-0879
dc.date.accessioned	2022-08-27T21:16:33Z
dc.date.available	2022-08-27T21:16:33Z
dc.date.issued	2018-11-01
dc.identifier.citation	ACM Transactions on Intelligent Systems and Technology, 2018, 9, (6)
dc.identifier.issn	2157-6904
dc.identifier.issn	2157-6912
dc.identifier.uri	http://hdl.handle.net/10453/160953
dc.description.abstract	One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article proposes a novel adaptive self-advised online OCSVM that incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior samples in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly 4 improved the classification error rates, was able to assimilate the changes in the positive data distribution over time, and maintained a high damage detection accuracy in all case studies.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM Transactions on Intelligent Systems and Technology
dc.relation.isbasedon	10.1145/3230708
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.title	Adaptive online one-class support vector machines with applications in structural health monitoring
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-27T21:16:30Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	6

Abstract:

One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article proposes a novel adaptive self-advised online OCSVM that incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior samples in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly 4 improved the classification error rates, was able to assimilate the changes in the positive data distribution over time, and maintained a high damage detection accuracy in all case studies.

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http://hdl.handle.net/10453/160953