File Entry: Random Forest for Gene Selection and Microarray Data Classification

Created: 2012-07-24 04:44:00
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Version created on: 2012-07-24 04:44:00

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 A random forest method has been selected to perform both gene

selection and classification of the microarray data. The goal of this research is
to develop and improve the random forest gene selection method. Hence,
improved gene selection method using random forest has been proposed to
obtain the smallest subset of genes as well as biggest subset of genes prior to
classification. In this research, ten datasets that consists of different classes are
used, which are Adenocarcinoma, Brain, Breast (Class 2 and 3), Colon,
Leukemia, Lymphoma, NCI60, Prostate and Small Round Blue-Cell Tumor
(SRBCT). Enhanced random forest gene selection has performed better in terms
of selecting the smallest subset as well as biggest subset of informative genes
through gene selection. Furthermore, the classification performed on the
selected subset of genes using random forest has lead to lower prediction error
rates compared to existing method and other similar available methods.

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