I'm training a SVM classifier on a dataset using LibSVM through Weka, but after I have my trained classifier I'd like to know which features are important to my classifier and which features don't have much use.
Currently a pretty mediocre way I'm doing this is running supervised attribute selection in Weka using the Ranker search and some search method (not sure which one would be best, currently using CorrelationAttributeEval), but this isn't specific to my model, it's just stating how well correlated my features are to my classification attribute.
I'm not that familiar with SVM, but I'm aware that the trained classifier has some weight values for each attribute. Could I rank attributes by the absolute value of their weights and it'd tell me how well those attributes predict the classification attribute? If that's a valid method, is anyone also familiar with how I could do that (code wise) using LibSVM in Weka?