How to obtain features' weights

Question

I am dealing with highly imbalanced data set and my idea is to obtain values of feature weights from my libSVM model. As for now I am OK with the linear kernel, whe

Answer 1

This is not only impossible, as stated in the documentation:

Weights asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel.

but also it doesn't make sense. In linear SVM the resulting separating plane is in the same space as your input features. Therefore its coefficients can be viewed as weights of the input's "dimensions".

In other kernels, the separating plane exists in another space - a result of kernel transformation of the original space. Its coefficients are not directly related to the input space. In fact, for the rbf kernel the transformed space is infinite-dimensional (you can get a starting point on this on Wikipedia of course).