Take the tour ×  I have a MLP with multiple nodes in its output
layer which is predicting membership of classes, one class per
output node. I am currently using a "winner takes all" rule for
determining which output to select as the predicted class, but
intuitively I feel like classes with low membership which have a
similar probability of membership for an observation should
probably be chosen in favour of large classes.  Are there any
accepted algorithms for choosing an output which take into
consideration the class populations in the original data?
share|improve this question Simon, I don't know why you haven't
got any answers yet. It may be because there are no experts
round here, or because the experts don't know the answer. But
one thing that might improve your chances of getting a good
answer is if you clarified the question a bit for non-experts.
E.g. what does MLP stand for? Can you give us some links to
sources that give background on your question? Can you tell us
some similar problems that have b