The Influence of Prior Knowledge on the Expected Performance of a Classifier

In this talk, we study probabilistic properties of pattern classifiers in discrete feature space. The principle of Bayesian averaging of recognition performance is used for the analysis. We consider two cases: a) prior probabilities of classes are unknown, and b) prior probabilities of classes are known. The misclassification probability is represented as a random value, for which the characteristic function (expressed via Kummer hypergeometric function) and absolute moments are analytically derived. For the case of unknown priors, an approximate formula for calculation of sufficient learning sample size is obtained. The comparison between the performances for two considered cases is made. As an example, we consider the problem of mutational hotspots classification in genetic sequences.