A lazy bagging approach to classification

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dc.contributor.author Zhu, Xingquan en_US
dc.contributor.author Yang, Y en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T09:00:44Z
dc.date.available 2012-02-02T09:00:44Z
dc.date.issued 2008 en_US
dc.identifier 2011000593 en_US
dc.identifier.citation Zhu Xingquan and Yang Y 2008, 'A lazy bagging approach to classification', Pergamon-Elsevier Science Ltd, vol. 41, no. 10, pp. 2980-2992. en_US
dc.identifier.issn 0031-3203 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/15064
dc.description.abstract in this paper, we propose lazy bagging (LB), which builds bootstrap replicate bags based on the characteristics of test instances. Upon receiving a test instance X-k, LB trims bootstrap bags by taking into consideration X-k's nearest neighbors in the tra en_US
dc.language en_US
dc.publisher Pergamon-Elsevier Science Ltd en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.patcog.2008.03.008 en_US
dc.title A lazy bagging approach to classification en_US
dc.parent Pattern Recognition en_US
dc.journal.volume 41 en_US
dc.journal.number 10 en_US
dc.publocation Oxford en_US
dc.identifier.startpage 2980 en_US
dc.identifier.endpage 2992 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 107283 en_US
dc.personcode 0000072848 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing 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 classification; classifier ensemble; bagging; lazy learning en_US
dc.staffid en_US

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