Adaptive K-Local Hyperplane (AKLH) Classifiers on Semantic Spaces to Determine Health Consumer Webpage Metadata

Chen, G; Warren, J; Yang, T; Kecman, V

Adaptive K-Local Hyperplane (AKLH) Classifiers on Semantic Spaces to Determine Health Consumer Webpage Metadata

Chen, G Warren, J Yang, T Kecman, V

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of The 21th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS), 2008, pp. 287 - 289
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Chen, G	en_US
dc.contributor.author	Warren, J	en_US
dc.contributor.author	Yang, T	en_US
dc.contributor.author	Kecman, V	en_US
dc.contributor.editor	Seppo Puuronen	en_US
dc.date	2008-07-15	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Proceedings of The 21th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS), 2008, pp. 287 - 289	en_US
dc.identifier.isbn	978-0-7695-3165-6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16195
dc.description.abstract	In this paper we look at automated classification to determine a metadata attribute related to the 'tone' of a consumer-oriented breast cancer Webpage as medical or supportive. We use a semantic space model called hyperspace analog to language (HAL), based on word co-occurrence, to provide features for webpage classification. Adaptive k-local hyperplane (AKLH), an extension of k nearest neighbour, is then applied to training and testing data. We observe 92% classification accuracy on test cases. This combination of methods appears promising for identifying non-trivial metadata attributes of consumer health webpages, with potential use embedded in a search engine or as a meta-data coding support tool.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of The 21th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS)	en_US
dc.relation.ispartof	IEEE International Symposium on Computer-Based Medical Systems	en_US
dc.relation.isbasedon	10.1109/CBMS.2008.84	en_US
dc.rights	© 2008 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.title	Adaptive K-Local Hyperplane (AKLH) Classifiers on Semantic Spaces to Determine Health Consumer Webpage Metadata	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Jyvaskyla, Finland	en_US
utslib.for	0803 Computer Software	en_US
dc.location.activity	Jyvaskyla, Finland	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
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.start-date	2008-07-15	en_US

Abstract:

In this paper we look at automated classification to determine a metadata attribute related to the 'tone' of a consumer-oriented breast cancer Webpage as medical or supportive. We use a semantic space model called hyperspace analog to language (HAL), based on word co-occurrence, to provide features for webpage classification. Adaptive k-local hyperplane (AKLH), an extension of k nearest neighbour, is then applied to training and testing data. We observe 92% classification accuracy on test cases. This combination of methods appears promising for identifying non-trivial metadata attributes of consumer health webpages, with potential use embedded in a search engine or as a meta-data coding support tool.

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http://hdl.handle.net/10453/16195