  An algorithm for optimization of signal significance or any other
classification figure of merit suited for analysis of high energy physics (HEP)
data is described. This algorithm trains decision trees on many bootstrap
replicas of training data with each tree required to optimize the signal
significance or any other chosen figure of merit. New data are then classified
by a simple majority vote of the built trees. The performance of this algorithm
has been studied using a search for the radiative leptonic decay B->gamma l nu
at BaBar and shown to be superior to that of all other attempted classifiers
including such powerful methods as boosted decision trees. In the B->gamma e nu
channel, the described algorithm increases the expected signal significance
from 2.4 sigma obtained by an original method designed for the B->gamma l nu
analysis to 3.0 sigma.
