Adaptive local hyperplane algorithm for learning small medical data sets

Yang, T; Kecman, V

Adaptive local hyperplane algorithm for learning small medical data sets

Yang, T Kecman, V

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Publisher:: Blackwell Publishing Ltd
Publication Type:: Journal Article
Citation:: Expert Systems, 2009, 26 (4), pp. 355 - 359
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Yang, T	en_US
dc.contributor.author	Kecman, V	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Expert Systems, 2009, 26 (4), pp. 355 - 359	en_US
dc.identifier.issn	0266-4720	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13485
dc.description.abstract	It is not unique that only a few samples from medical studies are available for knowledge discovery. Hence, a suitable classifier for the small data set learning problem is very interesting in medical work. In this paper, we experiment with the adaptive local hyperplane algorithm on small medical data sets. The experimental results on two cancer data sets demonstrate that the proposed classifier outperforms, on average, all the other four benchmarking classifiers for learning small data sets.	en_US
dc.publisher	Blackwell Publishing Ltd	en_US
dc.relation.ispartof	Expert Systems	en_US
dc.relation.isbasedon	10.1111/j.1468-0394.2009.00494.x	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Adaptive local hyperplane algorithm for learning small medical data sets	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	26	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	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.issue	4	en_US
pubs.volume	26	en_US

Abstract:

It is not unique that only a few samples from medical studies are available for knowledge discovery. Hence, a suitable classifier for the small data set learning problem is very interesting in medical work. In this paper, we experiment with the adaptive local hyperplane algorithm on small medical data sets. The experimental results on two cancer data sets demonstrate that the proposed classifier outperforms, on average, all the other four benchmarking classifiers for learning small data sets.

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http://hdl.handle.net/10453/13485