I have a data set of a bag of words. I randomly choose some points and use them for testing and the others are used for training.
- case (1) I just take each data-point from the test set and classify it as having the same class label as its nearest point from the train set.
- case (2) I do the classification using any known supervised classifier.
I always get better recognition rate in case (1). That is, not doing any learning at all, is better than using any supervised learning, for this data set (and others) ! Is that a frequent situation ?