Classification procedure using Minimum Redundancy Maximum Relevance (MRMR) After selecting features with MRMR (by quantizing original feature space of training data), should we classify the test data using quantized values or original values?
If you are not using the quantized values, what was the point of using the MRMR? from what I understand, it helps you "select a feature subset set that best characterizes the statistical property of a target classification variable" (sounds like PCA). So after you have done that, it helps your classifier to focus on the more important features.