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Bioinformatics Advance Access originally published online on April 27, 2009
Bioinformatics 2009 25(13):1711-1712; doi:10.1093/bioinformatics/btp286
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

penalizedSVM: a R-package for feature selection SVM classification

Natalia Becker 1,*, Wiebke Werft 2, Grischa Toedt 1, Peter Lichter 1 and Axel Benner 2

1Division Molecular Genetics and 2Division Biostatistics, INF 280, 69120 Heidelberg, Germany.

*To whom correspondence should be addressed.


  Abstract

Summary: Support vector machine (SVMs) classification is a widely used and one of the most powerful classification techniques. However, a major limitation is that SVM cannot perform automatic gene selection. To overcome this restriction, a number of penalized feature selection methods have been proposed. In the R package ‘penalizedSVM’ implemented penalization functions L1 norm and Smoothly Clipped Absolute Deviation (SCAD) provide automatic feature selection for SVM classification tasks.

Availability: The R package ‘penalizedSVM’ is available from the Comprehensive R Archive Network (http://cran.r-project.org/) under GPL-2 or later.

Contact: natalia.becker{at}dkfz.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Jonathan Wren

Received on February 20, 2009; revised on April 6, 2009; accepted on April 22, 2009

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