Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees

Details Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees

2006-10-31

arxiv.org/abs/physics/0610276v2

Nucl. Instrum. & Meth. A 574 (2007) 342-349

Physics

Data Analysis, Statistics and Probability

23 pages, 13 figures

Scientific paper

10.1016/j.nima.2007.02.081

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.

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