Continuous Support Vector Regression for Nonstationary Streaming Data.

Yu, H; Lu, J; Zhang, G

Continuous Support Vector Regression for Nonstationary Streaming Data.

Yu, H

Lu, J Zhang, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE transactions on cybernetics, 2020, PP, (99), pp. 1-14
Issue Date:: 2020-09-10

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Field	Value	Language
dc.contributor.author	Yu, H https://orcid.org/0000-0003-3444-9992
dc.contributor.author	Lu, J
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.date.accessioned	2020-11-14T19:15:14Z
dc.date.available	2020-11-14T19:15:14Z
dc.date.issued	2020-09-10
dc.identifier.citation	IEEE transactions on cybernetics, 2020, PP, (99), pp. 1-14
dc.identifier.issn	2168-2267
dc.identifier.issn	2168-2275
dc.identifier.uri	http://hdl.handle.net/10453/144004
dc.description.abstract	Quadratic programming is the process of solving a special type of mathematical optimization problem. Recent advances in online solutions for quadratic programming problems (QPPs) have created opportunities to widen the scope of applications for support vector regression (SVR). In this vein, efforts to make SVR compatible with streaming data have been met with substantial success. However, streaming data with concept drift remain problematic because the trained prediction function in SVR tends to drift as the data distribution drifts. Aiming to contribute a solution to this aspect of SVR's advancement, we have developed continuous SVR (C-SVR) to solve regression problems with nonstationary streaming data, that is, data where the optimal input-output prediction function can drift over time. The basic idea of C-SVR is to continuously learn a series of input-output functions over a series of time windows to make predictions about different periods. However, strikingly, the learning process in different time windows is not independent. An additional similarity term in the QPP, which is solved incrementally, threads the various input-output functions together by conveying some learned knowledge through consecutive time windows. How much learned knowledge is transferred is determined by the extent of the concept drift. Experimental evaluations with both synthetic and real-world datasets indicate that C-SVR has better performance than most existing methods for nonstationary streaming data regression.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP190101733
dc.relation.ispartof	IEEE transactions on cybernetics
dc.relation.isbasedon	10.1109/tcyb.2020.3015266
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Continuous Support Vector Regression for Nonstationary Streaming Data.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-14T19:15:09Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Quadratic programming is the process of solving a special type of mathematical optimization problem. Recent advances in online solutions for quadratic programming problems (QPPs) have created opportunities to widen the scope of applications for support vector regression (SVR). In this vein, efforts to make SVR compatible with streaming data have been met with substantial success. However, streaming data with concept drift remain problematic because the trained prediction function in SVR tends to drift as the data distribution drifts. Aiming to contribute a solution to this aspect of SVR's advancement, we have developed continuous SVR (C-SVR) to solve regression problems with nonstationary streaming data, that is, data where the optimal input-output prediction function can drift over time. The basic idea of C-SVR is to continuously learn a series of input-output functions over a series of time windows to make predictions about different periods. However, strikingly, the learning process in different time windows is not independent. An additional similarity term in the QPP, which is solved incrementally, threads the various input-output functions together by conveying some learned knowledge through consecutive time windows. How much learned knowledge is transferred is determined by the extent of the concept drift. Experimental evaluations with both synthetic and real-world datasets indicate that C-SVR has better performance than most existing methods for nonstationary streaming data regression.

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http://hdl.handle.net/10453/144004