Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees
数据分析、统计与概率
2007-05-23 v2
摘要
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.
引用
@article{arxiv.physics/0610276,
title = {Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees},
author = {Hai-Jun Yang and Byron P. Roe and Ji Zhu},
journal= {arXiv preprint arXiv:physics/0610276},
year = {2007}
}
备注
23 pages, 13 figures