Discriminant Analysis When the Initial Samples are Misclassified II: Non-Random Misclassification Models

Two models of non-random initial misclassifications are studied. In these models, observations which are closer to the mean of the “wrong” population have a greater chance of being misclassified than others. Sampling studies show that (a) the actual error rates of the rules from samples with initial misclassification are only slightly affected; (b) the apparent error rates, obtained by resubstituting the observations into the calculated discriminant function, are drastically affected, and cannot be used; and (c) the Mahalanobis D 2 is greatly inflated.