  Imbalanced data sets containing much more background than signal instances
are very common in particle physics, and will also be characteristic for the
upcoming analyses of LHC data. Following up the work presented at ACAT 2008, we
use the multivariate technique presented there (a rule growing algorithm with
the meta-methods bagging and instance weighting) on much more imbalanced data
sets, especially a selection of D0 decays without the use of particle
identification. It turns out that the quality of the result strongly depends on
the number of background instances used for training. We discuss methods to
exploit this in order to improve the results significantly, and how to handle
and reduce the size of large training sets without loss of result quality in
general. We will also comment on how to take into account statistical
fluctuation in receiver operation characteristic curves (ROC) for comparing
classifier methods.
