[R] Recursive Feature Elimination with SVM

Priyanka Purkayastha
Wed, 02 Jan 2019 01:31:56 -0000

Thankyou David.. I tried the same, I gave x as the data matrix and y as the
class label. But it returned an empty "featureRankedList". I get no
output
when I try the code.

On Tue, 1 Jan 2019 at 11:42 PM, David Winsemius <dwinsemius at
comcast.net>
wrote:

>
> On 1/1/19 4:40 AM, Priyanka Purkayastha wrote:
> > I have a dataset (data) with 700 rows and 7000 columns. I am trying to
do
> > recursive feature selection with the SVM model. A quick google search
> > helped me get a code for a recursive search with SVM. However, I am
> unable
> > to understand the first part of the code, How do I introduce my
dataset
> in
> > the code?
>
>
> Generally the "labels" is given to such a machine learning device
as the
> y argument, while the "features" are passed as a matrix to the x
argument.
>
>
> --
>
> David.
>
> >
> > If the dataset is a matrix, named data. Please give me an example for
> > recursive feature selection with SVM. Bellow is the code I got for
> > recursive feature search.
> >
> >      svmrfeFeatureRanking = function(x,y){
> >
> >      #Checking for the variables
> >      stopifnot(!is.null(x) == TRUE, !is.null(y) == TRUE)
> >
> >      n = ncol(x)
> >      survivingFeaturesIndexes = seq_len(n)
> >      featureRankedList = vector(length=n)
> >      rankedFeatureIndex = n
> >
> >      while(length(survivingFeaturesIndexes)>0){
> >      #train the support vector machine
> >      svmModel = svm(x[, survivingFeaturesIndexes], y, cost = 10,
> > cachesize=500,
> >                  scale=FALSE, type="C-classification",
kernel="linear" )
> >
> >      #compute the weight vector
> >      w = t(svmModel$coefs)%*%svmModel$SV
> >
> >      #compute ranking criteria
> >      rankingCriteria = w * w
> >
> >      #rank the features
> >      ranking = sort(rankingCriteria, index.return = TRUE)$ix
> >
> >      #update feature ranked list
> >      featureRankedList[rankedFeatureIndex] > >
survivingFeaturesIndexes[ranking[1]]
> >      rankedFeatureIndex = rankedFeatureIndex - 1
> >
> >      #eliminate the feature with smallest ranking criterion
> >      (survivingFeaturesIndexes =
survivingFeaturesIndexes[-ranking[1]])}
> >      return (featureRankedList)}
> >
> >
> >
> > I tried taking an idea from the above code and incorporate the idea in
my
> > code as shown below
> >
> >      library(e1071)
> >      library(caret)
> >
> >      data<- read.csv("matrix.csv", header = TRUE)
> >
> >      x <- data
> >      y <- as.factor(data$Class)
> >
> >      svmrfeFeatureRanking = function(x,y){
> >
> >        #Checking for the variables
> >        stopifnot(!is.null(x) == TRUE, !is.null(y) == TRUE)
> >
> >        n = ncol(x)
> >        survivingFeaturesIndexes = seq_len(n)
> >        featureRankedList = vector(length=n)
> >        rankedFeatureIndex = n
> >
> >        while(length(survivingFeaturesIndexes)>0){
> >          #train the support vector machine
> >          svmModel = svm(x[, survivingFeaturesIndexes], y,
cross=10,cost > > 10, type="C-classification",
kernel="linear" )
> >
> >          #compute the weight vector
> >          w = t(svmModel$coefs)%*%svmModel$SV
> >
> >          #compute ranking criteria
> >          rankingCriteria = w * w
> >
> >          #rank the features
> >          ranking = sort(rankingCriteria, index.return = TRUE)$ix
> >
> >          #update feature ranked list
> >          featureRankedList[rankedFeatureIndex] > >
survivingFeaturesIndexes[ranking[1]]
> >          rankedFeatureIndex = rankedFeatureIndex - 1
> >
> >          #eliminate the feature with smallest ranking criterion
> >          (survivingFeaturesIndexes >
survivingFeaturesIndexes[-ranking[1]])}
> >
> >        return (featureRankedList)}
> >
> > But couldn't do anything at the stage "update feature ranked
list"
> > Please guide
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
--
Regards,

Priyanka Purkayastha, M.Tech, Ph.D.,
SERB National Postdoctoral Researcher
Genomics and Systems Biology Lab,
Department of Chemical Engineering,
Indian Institute of Technology Bombay (IITB),
Powai, Mumbai- 400076

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