Feature selection for high dimensional microarray gene expression data via weighted signal to noise...
Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio
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Publisher
United States: Public Library of Science
Journal title
Language
English
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Publisher
United States: Public Library of Science
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Scope and Contents
Contents
Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio (WSNR) by exploiting the weights of features based on support...
Alternative Titles
Full title
Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_plos_journals_2806039501
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2806039501
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0284619
