IDENTIFICATION OF SIGNIFICANT FEATURES USING RANDOM FOREST FOR HIGH DIMENSIONAL MICROARRAY DATA

IDENTIFICATION OF SIGNIFICANT FEATURES USING RANDOM FOREST FOR HIGH DIMENSIONAL MICROARRAY DATA

Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. At the same time, selecting the relevant genes (features) from the high dimensional data can improve the classification accuracy of the learning algorithm....

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Bibliographic Details
Main Authors: ARPITA NAGPAL, VIJENDRA SINGH
Format: Article
Language:English
Published: Taylor's University 2018-08-01
Series:Journal of Engineering Science and Technology
Subjects:
Classification
Feature selection
High dimensional data
Microarray data
Random forest
Online Access:http://jestec.taylors.edu.my/Vol%2013%20issue%208%20August%202018/13_8_13.pdf

Internet

http://jestec.taylors.edu.my/Vol%2013%20issue%208%20August%202018/13_8_13.pdf

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