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

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dc.creatorYang, Hai-Jun
dc.creatorRoe, Byron P.
dc.creatorZhu, Ji
dc.date2006-10-31
dc.date2007-02-08
dc.date.accessioned2026-07-07T08:00:32Z
dc.date.available2026-07-07T08:00:32Z
dc.descriptionIn 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.
dc.description23 pages, 13 figures
dc.identifierhttps://arxiv.org/abs/physics/0610276
dc.identifierhttp://arxiv.org/abs/physics/0610276
dc.identifierNucl. Instrum. & Meth. A 574 (2007) 342-349
dc.identifierdoi:10.1016/j.nima.2007.02.081
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/128687
dc.subjectData Analysis, Statistics and Probability
dc.titleStudies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees
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