I have a dataset of samples belonging to >100 classes. I want to classify and/or cluster these classes. I have the following questions:
1) Is one classifier efficient for such problem? or one classifier for each one/subset of classes? (From my point of view: the efficient solution is to discover the features discriminate each class from all others and solve the problem as 1-to-all classification problem. Any suggestion on that?)
2) About 60% of these classes have 1 or 2 samples at maximum!. How can I create new samples from these 1-sample classes. Do you think any of SMOTE (synthetic minority oversampling technique) techniques are workable in this case.