  In this note we give an example application of a recently presented
predictive learning method called Rule Ensembles. The application we present is
the search for super-symmetric particles at the Large Hadron Collider. In
particular, we consider the problem of separating the background coming from
top quark production from the signal of super-symmetric particles. The method
is based on an expansion of base learners, each learner being a rule, i.e. a
combination of cuts in the variable space describing signal and background.
These rules are generated from an ensemble of decision trees. One of the
results of the method is a set of rules (cuts) ordered according to their
importance, which gives useful tools for diagnosis of the model. We also
compare the method to a number of other multivariate methods, in particular
Artificial Neural Networks, the likelihood method and the recently presented
boosted decision tree method. We find better performance of Rule Ensembles in
all cases. For example for a given significance the amount of data needed to
claim SUSY discovery could be reduced by 15 % using Rule Ensembles as compared
to using a likelihood method.
