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

Abstract

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.

23 pages, 13 figures

References in corpus (3)

• Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
• Studies of Boosted Decision Trees for MiniBooNE Particle Identification
• StatPatternRecognition: A C++ Package for Statistical Analysis of High Energy Physics Data

Cited by in corpus (2)

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