  This paper proposes a systematic framework to design a classification model
that yields a classifier which optimizes a utility function based on prior
knowledge. Specifically, as the data size grows, we prove that the produced
classifier asymptotically converges to the optimal classifier, an extended
version of the Bayes rule, which maximizes the utility function. Therefore, we
provide a meaningful theoretical interpretation for modeling with the knowledge
incorporated. Our knowledge incorporation method allows domain experts to guide
the classifier towards correctly classifying data that they think to be more
significant.
