If you have a bayes classifier trained for a set of classes, how to detect if the output is significant enough to choose a class? It would be useful for detecting samples wich can't be asigned to a class. I have tried testing if the class probability is above mean+2*stddev of the probabilities of all the clases, but I don't think it will be robust.
You could consider log-likelihood ratios. Consider R(C) = log(P(C|D)/P(~C| D)
, where C
is the class, D
are the features. Then you probably want to ensure that R(C)
is greater than some positive amount.
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