In my opinion, if there exist $N$ classes and after classification you realize that only $M\leq N$ are present in your result, then it is intuitive to think that your classification can be improved by using only $M$ classes.
However this is dangerous as if your data is supposed to have $N$ classes, the fact that a certain set has $M$ classes does not mean you can assume that your classifier may have $M$ classes because you are missing information for future data.
First of all, and answering your question, I do not think your procedure is a proper policy.
On the other hand, if during classification you want to gather several classes into one (since they have few representatives for instance) you are using similar to 'pruning' in decision tree like a 'merge' of classes. Maybe that is the name you are looking for.