Adaptive local hyperplane algorithm for learning small medical data sets

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dc.contributor.author Yang, T
dc.contributor.author Kecman, V
dc.date.accessioned 2011-02-07T06:22:07Z
dc.date.issued 2009-01
dc.identifier.citation Expert Systems, 2009, 26 (4), pp. 355 - 359
dc.identifier.issn 0266-4720
dc.identifier.other C1UNSUBMIT 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.
dc.publisher Blackwell Publishing Ltd
dc.relation.isbasedon 10.1111/j.1468-0394.2009.00494.x
dc.title Adaptive local hyperplane algorithm for learning small medical data sets
dc.type Journal Article
dc.parent Expert Systems
dc.journal.volume 4
dc.journal.volume 26
dc.journal.number 4 en_US
dc.publocation Oxford, UK en_US
dc.identifier.startpage 355 en_US
dc.identifier.endpage 359 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080105 Expert Systems
dc.personcode 108195
dc.percentage 100 en_US
dc.classification.name Expert Systems en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords small data set learning ? classification ? cancer en_US
dc.description.keywords small data set learning classification cancer
pubs.embargo.period Not known
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
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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