# feature selection for SVM

So I have some experience when it comes to SVM mainly through a basic course in machine learning. However when it comes to features I've never needed to do any form of feature selection before.

The dataset that I have right now has around 40 features and about 650 data points. I've read about different feature selection techniques and decided to try out recursive feature elimination, RFE.

I've tried using the caret package for R and scikit for python 3 however I'm rather confused with the kernels used. It would seem that RFE is mostly done with a linear kernel so my question becomes if you can perform RFE with other kernels like radial, sigmoid and poly.?

So i guess is possible, but not implemented in scikit and R.
Yes, you can. It's not hard to implement. if you decide to use caret, look up train_model_list in caret(https://cran.r-project.org/web/packages/caret/caret.pdf) P164 - A List of Available Models in train, this section describes methods you can use such as Distance Weighted Discrimination with Polynomial Kernel (method = 'dwdPoly') and Distance Weighted Discrimination with Radial Basis Function Kernel (method = 'dwdRadial').
I also came across msvm-rfe package,http://www.colbyimaging.com/wiki/statistics/msvm-rfe, the example showed using RBF kernel doing RFE.