SVM Feature Selection in R

Question

I am training a SVM classifier. Right now, I have about 4000 features, but a lot of them are redundant/uninformative. I want to reduce the features in the model to about maybe 20-50. I would like to use greedy hill climbing, reducing the features by 1 each time. The removed feature should be the least important feature. After training an SVM, how do I get the ranking of the importance of the features? If I am using libsvm in R, how do I get the weight of each feature, or some other similar type of indicator of importance? Thanks!

This question Variable Importance from SVM from CrossValidated answers related question. — topchef
specifically, if I am using libsvm in the e1071 R package, how do I get the weight of each attribute? Thanks! — Josh Zhang

Doctor Dan Doctor Dan · Accepted Answer · 2013-06-27T19:14:05

I would reduce the dimensionality of the problem first using PCA (Principal Component Analysis), then apply SVM. See, e.g., Andrew Ng's lecture videos

SVM Feature Selection in R

1 Answers