R help - Dec 2013 - tune an support vector machine

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

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
> ______________________________________________
> R-help@r-project.org mailing list
> 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.
>
	[[alternative HTML version deleted]]