  This paper investigates two feature-scoring criteria that make use of
estimated class probabilities: one method proposed by \citet{shen} and a
complementary approach proposed below. We develop a theoretical framework to
analyze each criterion and show that both estimate the spread (across all
values of a given feature) of the probability that an example belongs to the
positive class. Based on our analysis, we predict when each scoring technique
will be advantageous over the other and give empirical results validating our
predictions.
