A SVM-based classification approach for early warning systems

Guangquan, Z; Xiaowei, Y; Jie, L

A SVM-based classification approach for early warning systems

Guangquan, Z Xiaowei, Y Jie, L

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Publication Type:: Conference Proceeding
Citation:: World Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference, 2008, pp. 549 - 554
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Guangquan, Z	en_US
dc.contributor.author	Xiaowei, Y	en_US
dc.contributor.author	Jie, L https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	World Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference, 2008, pp. 549 - 554	en_US
dc.identifier.isbn	981279946X	en_US
dc.identifier.isbn	9789812799463	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10762
dc.description.abstract	An early warning system (EWS) is a timely surveillance tool to identifies potential crises and generate warning signals at a relatively early stage. This study aims to improve the learning functions of an EWS through training it using support vector machine (SVM) techniques. An adaptive pruning algorithm of SVM classification is developed which can improve prediction ability of EWS. This algorithm also can handle multi-data sources, multi-sensitive values, multi-indicators, and multi-crises issues in EWSs.	en_US
dc.relation.ispartof	World Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference	en_US
dc.title	A SVM-based classification approach for early warning systems	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080105 Expert Systems	en_US
dc.location.activity	Madrid, Spain	en_US
pubs.embargo.period	Not known	en_US
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/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

An early warning system (EWS) is a timely surveillance tool to identifies potential crises and generate warning signals at a relatively early stage. This study aims to improve the learning functions of an EWS through training it using support vector machine (SVM) techniques. An adaptive pruning algorithm of SVM classification is developed which can improve prediction ability of EWS. This algorithm also can handle multi-data sources, multi-sensitive values, multi-indicators, and multi-crises issues in EWSs.

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http://hdl.handle.net/10453/10762