# Classification with increasing number of classes

I have dataset with large number of labelled data (say that there are k classes). I have also another, much smaller dataset with unlabelled data that I want also to be labelled. The problem is that in the second dataset, number of classes need not to be the same as number of classes in first dataset (more precisely, the there may be more classes). In other words, some objects from unlabelled dataset can be classified to one of k possible classes since they are "too close of them" but some objects should be clasified to new classes.

Real world example: In some applications, number of classes can increase with time. For example, number of known species increases with time, so when I see any unknown plant in nature, biologist can either to classify it or to say that this is new discovery (new class).

Is there any technique that deals with this type of classification? Any help is appreciated.

Therefore we may have hierarchical classifiers, the first one to check if the data is some new species, if no, then second classifier will categorize data to $$k$$ known classes.