  The majority of traditional classification ru les minimizing the expected
probability of error (0-1 loss) are inappropriate if the class probability
distributions are ill-defined or impossible to estimate. We argue that in such
cases class domains should be used instead of class distributions or densities
to construct a reliable decision function. Proposals are presented for some
evaluation criteria and classifier learning schemes, illustrated by an example.
