Journal Article

penalizedSVM: a R-package for feature selection SVM classification

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

in Bioinformatics

Volume 25, issue 13, pages 1711-1712
Published in print July 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp286
penalizedSVM: a R-package for feature selection SVM classification

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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@dkfz.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1119 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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