  In this paper, we compare the performance, stability and robustness of
Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT) using
MiniBooNE Monte Carlo samples. These methods attempt to classify events given a
number of identification variables. The BDT algorithm has been discussed by us
in previous publications. Testing is done in this paper by smearing and
shifting the input variables of testing samples. Based on these studies, BDT
has better particle identification performance than ANN. The degradation of the
classifications obtained by shifting or smearing variables of testing results
is smaller for BDT than for ANN.
