I'm working on a classification system of mine, but am needing help with the proper process order. Specifically, I'm using LibSVM and a range of feature sets extracted from my data. I'm wondering, though, if it makes more sense to...
a) Perform feature selection via Forward Selection and using default LibSVM to evaluate results
b) Perform a grid search to find LibSVM parameters while using feature selected data
OR the opposite
a) Perform grid search to find LibSVM parameters while using all features
b) Perform feature selection via Forward Selection and using parameters found in grid search
To me, it makes more sense to reduce features with Forward Selection and default SVM parameters. Then, hone the parameters with the selected features. Unfortunately, maybe I'm just not searching the right keywords to find answers on this topic x_x