Let`s say I have training data which contains 10 classes and have build a classifier using this data.
When applying this classifier in real life it may encounter examples not belong to the classes in the training data. I want to build a novelty detector to reject these examples. I consider using one-class SVM from sklearn and have 2 options:
- Using all training data as a positive class to train one-class SVM
- Train 10 one-class SVM model, one for each class in training data
Which way is better and why?