svmrfeFeatureRanking: R implementation of the SVM-RFE algorithm for binary...

In pijush1285/sigFeature: sigFeature: Significant feature selection using SVM-RFE & t-statistic

Description

To solve the classification problem with the help of ranking the features an algorithm was proposed by Guyon, Isabelle, et al. named SVM-RFE. In this algorithm the dataset has been trained with SVM linear kernel model and the feature containing the smallest ranking is removed. This criterion is the w value of the decision hyperplane given by the SVM.

Usage

    svmrfeFeatureRanking(x,y)

Arguments

x	x n-by-d data matrix to train (n samples/patients, d clones/genes)
y	y vector of class labels -1 or 1\'s (for n chips/patients )

Details

Adopted from R code: http://www.uccor.edu.ar/busquedas/?txt_palabra=seminarios

Value

returns the feature list.

Note

This function also rank the feature.

Author(s)

Guyon, Isabelle, et al.

References

Guyon, Isabelle, et al. "Gene selection for cancer classification using support vector machines." Machine learning 46.1-3 (2002): 389-422.

Zhang, H. H., Ahn, J., Lin, X. and Park, C. (2006). Gene selection using support vector machines with nonconvex penalty. Bioinformatics, 22, pp. 88-95.

See Also

scadsvc, predict.penSVM, sim.data

Examples

#Example for svmrfeFeatureRanking()
#Data set taken from GSE2280
library(SummarizedExperiment)
data(ExampleRawData, package="sigFeature")

x  <- t(assays(ExampleRawData)$counts)
y  <- colData(ExampleRawData)$sampleLabels

x <- x[ ,1:500]

#featureRankedList = svmrfeFeatureRanking(x,y)
print(featureRankedList[1:10])

#Train the data with ranked frature
#library(e1071)
#svmmodel = svm(x[ , featureRankedList[1:50]], y, cost = 10, kernel="linear")
#summary(svmmodel)

pijush1285/sigFeature documentation built on Sept. 11, 2021, 1:02 p.m.