A modified support vector regression: Integrated selection of training subset and model

Che, JX; Yang, YL; Li, L; Li, YY; Zhu, SL

A modified support vector regression: Integrated selection of training subset and model

Che, JX Yang, YL Li, L Li, YY Zhu, SL

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Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2017, 53 pp. 308 - 322
Issue Date:: 2017-04-01

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Field	Value	Language
dc.contributor.author	Che, JX	en_US
dc.contributor.author	Yang, YL	en_US
dc.contributor.author	Li, L	en_US
dc.contributor.author	Li, YY	en_US
dc.contributor.author	Zhu, SL	en_US
dc.date.accessioned	2018-04-24T06:22:40Z
dc.date.available	2018-04-24T06:22:40Z
dc.date.issued	2017-04-01	en_US
dc.identifier.citation	Applied Soft Computing Journal, 2017, 53 pp. 308 - 322	en_US
dc.identifier.issn	1568-4946	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123767
dc.description.abstract	© 2017 Elsevier B.V. In recent years, support vector regression (SVR) has become an emerging and popular forecasting technique in the field of machine learning. However, it is subjected to the model selection and learning complexity O(K N 3 ), especially for a massive data set (N is the size of training dataset, and K is the number of search). How to simultaneously reduce K and N can give us insight and inspiration on designing an effective and accurate selection algorithm. To this end, this paper tries to integrate the selection of training subset and model for SVR, and proposes a nested particle swarm optimization (NPSO) by inheriting the model selection of the existing training subset based SVR (TS-SVR). This nested algorithm is achieved by adaptively and periodically estimating the search region of the optimal parameter setting for TS-SVR. Complex SVR, involving large-scale training data, can be seen as extensions of TS-SVRs, yielding a nested sequence of TS-SVRs with increasing sample size. The uniform design idea is transplanted to the above modeling process, and the convergence for the proposed model is proofed. By using two artificial regression problems, Boston housing and electric load in New South Wales as empirical data, the proposed approach is compared with the standard ones, the APSO-OTS-SVR, and other existing approaches. Empirical results show that the proposed approach not only can select proper training subset and parameter, but also has better generalization performance and fewer processing time.	en_US
dc.relation.ispartof	Applied Soft Computing Journal	en_US
dc.relation.isbasedon	10.1016/j.asoc.2016.12.053	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A modified support vector regression: Integrated selection of training subset and model	en_US
dc.type	Journal Article
utslib.citation.volume	53	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence And Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
utslib.copyright.status	closed_access	*
pubs.declined	2018-04-24T16:22:40.600+1000
pubs.publication-status	Published	en_US
pubs.volume	53	en_US

Abstract:

© 2017 Elsevier B.V. In recent years, support vector regression (SVR) has become an emerging and popular forecasting technique in the field of machine learning. However, it is subjected to the model selection and learning complexity O(K N 3 ), especially for a massive data set (N is the size of training dataset, and K is the number of search). How to simultaneously reduce K and N can give us insight and inspiration on designing an effective and accurate selection algorithm. To this end, this paper tries to integrate the selection of training subset and model for SVR, and proposes a nested particle swarm optimization (NPSO) by inheriting the model selection of the existing training subset based SVR (TS-SVR). This nested algorithm is achieved by adaptively and periodically estimating the search region of the optimal parameter setting for TS-SVR. Complex SVR, involving large-scale training data, can be seen as extensions of TS-SVRs, yielding a nested sequence of TS-SVRs with increasing sample size. The uniform design idea is transplanted to the above modeling process, and the convergence for the proposed model is proofed. By using two artificial regression problems, Boston housing and electric load in New South Wales as empirical data, the proposed approach is compared with the standard ones, the APSO-OTS-SVR, and other existing approaches. Empirical results show that the proposed approach not only can select proper training subset and parameter, but also has better generalization performance and fewer processing time.

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http://hdl.handle.net/10453/123767