Efficient feature size reduction via predictive forward selection
Pattern Recognition
| Hi-index | 3.84 |
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.