Boosted decision trees as an alternative to artificial neural networks for particle identification
Boosted decision trees as an alternative to artificial neural networks for particle identification
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DOI：
10.1016/j.nima.2004.12.018
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发表时间：
2005-05-11
影响因子：
1.4
通讯作者：
McGregor, G
中科院分区：
文献类型：
--
作者：
Roe, BP;Yang, HJ;McGregor, G
使用人工神经网络和提升决策树的粒子识别的功效进行了比较。比较是在MiniBooNE的背景下进行的，这是费米实验室寻找中微子振荡的实验。基于模拟数据的Monte Carlo样本研究，在MiniBooNE实验中，Boosting算法的粒子识别性能优于人工神经网络。虽然本文中的测试是针对一个实验的，但预计提升算法将在物理学中得到广泛的应用。(c)2005 Elsevier B.V.保留所有权利。
The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics. (c) 2005 Elsevier B.V. All rights reserved.