Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1832103 | Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment | 2007 | 8 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Instrumentation
Authors
Hai-Jun Yang, Byron P. Roe, Ji Zhu,