  Feature or variable selection is a problem inherent to large data sets. While
many methods have been proposed to deal with this problem, some can scale
poorly with the number of predictors in a data set. Screening methods scale
linearly with the number of predictors by checking each predictor one at a
time, and are a tool used to decrease the number of variables to consider
before further analysis or variable selection. For classification, there is a
variety of techniques. There are parametric based screening tests, such as
t-test or SIS based screening, and non-parametric based screening tests, such
as Kolmogorov distance based screening, and MV-SIS. We propose a method for
variable screening that uses Bayesian-motivated tests, compare it to SIS based
screening, and provide example applications of the method on simulated and real
data. It is shown that our screening method can lead to improvements in
classification rate. This is so even when our method is used in conjunction
with a classifier, such as DART, which is designed to select a sparse subset of
variables. Finally, we propose a classifier based on kernel density estimates
that in some cases can produce dramatic improvements in classification rates
relative to DART.
