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 <balu...@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]] ______________________________________________ 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.
