I'm using euclidean distance for kNN. I have labeled data, I have took logarithm of some variables to make them look more like normaly distributed and scaled them all. And now I would like to multiply some variables by weights, then compute euclidean distance and train kNN. But how to find those weights ? My idea is to determine centers of classes this going to be set C, and then make optimization of kNN on set C by random search, I think that I can't do it on subset of training set, because it size would by to high or too small for accurate representation/sampling of dataset
Do you have any other ideas ? I don't think that changing parameters k and l going to have the same approach as mine or mayby does it ?