What is an example of an algorithm that, when i have a known distribution across discrete groups and I have some sort of model score that a person is in each group, assigns persons to groups such that the sum of the group scores are maximized, or some other objective criteria is met and we honor the known class distribution.
I have some code that assigns classified units to the group associated with the max score for the unit one by one until a group is "full". It starts with the highest scoring units, which creates an issue, as groups with a higher class probability in the predictions than the known end up being dominated by predictive units that generate a lot of data (e.g. long documents).
Has anyone else run into this or considered this before in classification problems?