Hi Uwe,

It looks SVM in e1071 and Kernlab does not support feature selection, but
you can take a look at package penalizedSVM (
http://cran.r-project.org/web/packages/penalizedSVM/penalizedSVM.pdf).

Or you can implement a SVM-RFE (
http://axon.cs.byu.edu/Dan/778/papers/Feature%20Selection/guyon*.pdf) by
the alpha values returned by svm() in e1071 or ksvm() in Kernlab.

Wuming


On Fri, Dec 6, 2013 at 7:06 AM, Uwe Bohne <balu555@gmx.de> wrote:

>
>    Hej all,
>
>    actually i try to tune a SVM in R and use the package "e1071" wich works
>    pretty well.
>    I do some gridsearch in the parameters and get the best possible
> parameters
>    for classification.
>    Here is my sample code
>
>    type<-sample(c(-1,1) , 20, replace = TRUE )
>    weight<-sample(c(20:50),20, replace=TRUE)
>    height<-sample(c(100:200),20, replace=TRUE)
>    width<-sample(c(30:50),20,replace=TRUE)
>    volume<-sample(c(1000:5000),20,replace=TRUE)
>
>    data<-cbind(type,weight,height,width,volume)
>    train<-as.data.frame(data)
>    library("e1071")
>
>    features <- c("weight","height","width","volume")
>    (formula<-as.formula(paste("type ~ ", paste(features, collapse= "+"))))
>
>    svmtune=tune.svm(formula,  data=train, kernel="radial", cost=2^(-2:5),
>    gamma=2^(-2:1),cross=10)
>    summary(svmtune)
>
>    My question is if there is a way to tune the features.
>
>    So in other words - what i wanna do is to try all possible combinations
> of
>    features : for example use only (volume) or use (weight, height) or use
>    (height,volume,width) and so on for the SVM  and to get the best
> combination
>    back.
>
>
>    Best wishes
>
>    Uwe
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