Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23846
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorFung, BYM-
dc.creatorNg, VTY-
dc.date.accessioned2015-09-30T09:42:49Z-
dc.date.available2015-09-30T09:42:49Z-
dc.identifier.isbn0-7695-2108-8-
dc.identifier.urihttp://hdl.handle.net/10397/23846-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectCanceren_US
dc.subjectClassificationen_US
dc.subjectLungen_US
dc.subjectMedical administrative data processingen_US
dc.titleImproving classification performance for heterogeneous cancer gene expression dataen_US
dc.typeConference Paperen_US
dc.identifier.spage131-
dc.identifier.epage132-
dc.identifier.volume2-
dc.identifier.doi10.1109/ITCC.2004.1286608-
dcterms.abstractIn our previous work, we proposed the "impact factors" (IFs) to measure the symmetric errors in different microarray experiments, and integrated the IFs to the Golub and Slonim (GS) and k-nearest neighbors (kNN) classifiers. In this paper, we perform experiments with different cancer types, which are lung adenocarcinomas and prostate cancer, to further validate the efficiency and effectiveness of the IFs integrations in terms of measurements of classification accuracy, sensitivity and specificity. For both cancer types, the IFs integrations can be successfully improved on the classification performance.-
dcterms.bibliographicCitationITCC 2004 : International Conference on Information Technology, Coding and Computing : proceedings : April 5-7, 2004, Las Vegas, Nevada, v. 2, p. 131-132-
dcterms.issued2004-
dc.relation.ispartofbookITCC 2004 : International Conference on Information Technology, Coding and Computing : proceedings : April 5-7, 2004, Las Vegas, Nevada-
dc.identifier.rosgroupidr17019-
dc.description.ros2003-2004 > Academic research: refereed > Refereed conference paper-
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