  Suppose that the only available information in a multi-class problem are
expert estimates of the conditional probabilities of occurrence for a set of
binary features. The aim is to select a subset of features to be measured in
subsequent data collection experiments. In the lack of any information about
the dependencies between the features, we assume that all features are
conditionally independent and hence choose the Naive Bayes classifier as the
optimal classifier for the problem. Even in this (seemingly trivial) case of
complete knowledge of the distributions, choosing an optimal feature subset is
not straightforward. We discuss the properties and implementation details of
Sequential Forward Selection (SFS) as a feature selection procedure for the
current problem. A sensitivity analysis was carried out to investigate whether
the same features are selected when the probabilities vary around the estimated
values. The procedure is illustrated with a set of probability estimates for
Scrapie in sheep.
