In real world applications very often the entire set of classes is not known during the training phase (e.g. identifying objects, sounds, etc.). A system is needed that can classify observations into the predefined classes but also tells the user that there is an observation that probably is related to a completely new class which was not in the training data - sort of a combination of classification and anomaly detection.
I saw that this problem is also called "open world classification" or "open set classification" with a few papers addressing this issue.
A simpler approach would be just take the prediction probability of a neural network and interpret a low probability as an indication of a new class.
Do you see any problem with the latter approach? What is your opinion about that "open world classification" research?