Adaptive local hyperplane algorithm for learning small medical data sets

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dc.contributor.author Yang, Tao en_US
dc.contributor.author Kecman, Vojislav en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:22:07Z
dc.date.available 2011-02-07T06:22:07Z
dc.date.issued 2009 en_US
dc.identifier 2009002150 en_US
dc.identifier.citation Yang Tao and Kecman Vojislav 2009, 'Adaptive local hyperplane algorithm for learning small medical data sets', Blackwell Publishing Ltd, vol. 26, no. 4, pp. 355-359. en_US
dc.identifier.issn 0266-4720 en_US
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. en_US
dc.language en_US
dc.publisher Blackwell Publishing Ltd en_US
dc.relation.isbasedon http://dx.doi.org/10.1111/j.1468-0394.2009.00494.x en_US
dc.title Adaptive local hyperplane algorithm for learning small medical data sets en_US
dc.parent Expert Systems en_US
dc.journal.volume 26 en_US
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 en_US
dc.personcode 108195 en_US
dc.personcode 0000059430 en_US
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.staffid en_US


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