Where is the difference between one-class, binary-class and multinominal-class classification?
If I like to classify text in lets say four classes and also want the system to be able to tell me that none of these classes matches the unknown/untrained test-data.
Couldn't I just use all the methods that I mentioned above to reach my goal? e.g. I could describe C1, C2, C3 and C4 as four different trainings-sets for binary-classification and use the trained models to label an unknow data-set ...
Just by saying, Training-Set for C1 contains class 1 (all good samples for C1) and class 0 (mix of all C2, C3 and C4 as bad samples for C1).
Is unlabeled-data C1 -> 1 or 0
Is unlabeled-data C2 -> 1 or 0 ... and so on ...
For multinominal classification I could just define a training-set containing all good sample data for C1, C2, C3 and C4 in one training-set and then use the one resulting model for classification ...
But where is the difference between this two methods? (except of that I have to use different algorithms)
And how would I define a training-set for the described problem of categorizing data in those four classes using one-class classfication (is that even possible)?
Excuse me if I'm completely wrong in my thinking. Would appreciate an answer that makes the methodology a little bit clearer to me =)