Sample size determination of binomial data with the presence of misclassification

We propose a double sampling scheme with two classifiers to address the problem of optimal sample size when misclassification among binomial observations is observed. The classifiers vary with respect to the classifying cost and precision. Furthermore, since the data are unknown, an additional constraint is set on the probability of observing ``undesirable'' data. The method is developed following the Bayesian point of view.