Joint sub-classifiers one class classification model for avian influenza outbreak detection

Zhang, J; Lu, J; Zhang, G

Joint sub-classifiers one class classification model for avian influenza outbreak detection

Zhang, J Lu, J

Zhang, G

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Publication Type:: Journal Article
Citation:: International Journal of Computational Intelligence and Applications, 2011, 10 (4), pp. 425 - 443
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	International Journal of Computational Intelligence and Applications, 2011, 10 (4), pp. 425 - 443	en_US
dc.identifier.issn	1469-0268	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18280
dc.description.abstract	H5N1 avian influenza outbreak detection is a significant issue for early warning of epidemics. This paper proposes domain knowledge-based joint one class classification model for avian influenza outbreak. Instead of focusing on manipulations of the one class classification model, we delve into the one class avian influenza dataset, divide it into sub-classes by domain knowledge, train the sub-class classifiers and unify the result of each classifier. The proposed joint method solves the one class classification and features selection problems together. The experiment results demonstrate that the proposed joint model definitely outperforms the normal one class classification model on the animal avian influenza dataset. © 2011 Imperial College Press.	en_US
dc.relation.ispartof	International Journal of Computational Intelligence and Applications	en_US
dc.relation.isbasedon	10.1142/S1469026811003173	en_US
dc.title	Joint sub-classifiers one class classification model for avian influenza outbreak detection	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	10	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
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	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

H5N1 avian influenza outbreak detection is a significant issue for early warning of epidemics. This paper proposes domain knowledge-based joint one class classification model for avian influenza outbreak. Instead of focusing on manipulations of the one class classification model, we delve into the one class avian influenza dataset, divide it into sub-classes by domain knowledge, train the sub-class classifiers and unify the result of each classifier. The proposed joint method solves the one class classification and features selection problems together. The experiment results demonstrate that the proposed joint model definitely outperforms the normal one class classification model on the animal avian influenza dataset. © 2011 Imperial College Press.

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http://hdl.handle.net/10453/18280