Classification Of Microarray Datasets Using Random Forest

Simple item page
dc.contributor.authorNg, Ee Ling
dc.date.accessioned2022-02-09T07:09:30Z
dc.date.available2022-02-09T07:09:30Z
dc.date.issued2009-06
dc.description.abstractDNA microarray technology has enabled the capability to monitor the expressions of tens of thousands of genes in a biological sample on a single chip. Medical fields can benefit from microarray data mining as it helps in early detection of genes mutation and diagnosis of disease. A well built model can be used to predict unknown disease classes in a test case. Prior to a well built model is to achieve good classification results which rely very much on the classifiers that are being used. However, in most microarray data, the number of genes usually outnumbers the number of samples.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/14617
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectMicroarrayen_US
dc.subjectRandom Foresten_US
dc.titleClassification Of Microarray Datasets Using Random Foresten_US
dc.typeThesisen_US
Files
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Pusat Pengajian Sains Matematik - Tesis