Suppose I have a large number of distinct classes, some of which are related.

My model has high classification accuracy for some classes, whilst other classes are hard to predict.

How could I group together similar classes such that accuracy improves, whilst maintaining as many distinct classes as possible?

An exhaustive search over the space of combinations is intractable.