基于Bagging决策树的信号重要性优化
Optimization of Signal Significance by Bagging Decision Trees
摘要：
描述了一种用于优化信号显著性或适合于分析高能物理（HEP）数据的任何其他分类优值的算法。该算法在训练数据的许多引导副本上训练决策树，每个树都需要优化信号显著性或任何其他选择的品质因数。然后，新数据通过构建的树的简单多数投票进行分类。该算法的性能已被研究，使用搜索的辐射轻子衰变B-> γ l nu在BaBar和被证明是优于所有其他尝试的分类器，包括这样的强大的方法，如提升决策树。在B->gamma e nu通道中，所描述的算法将预期信号显著性从通过为B->gamma l nu分析设计的原始方法获得的2.4西格玛增加到3.0西格玛。
英文摘要：
An algorithm for optimization of signal significance or any other classification figure of merit suited for analysis of high energy physics (HEP) data is described. This algorithm trains decision trees on many bootstrap replicas of training data with each tree required to optimize the signal significance or any other chosen figure of merit. New data are then classified by a simple majority vote of the built trees. The performance of this algorithm has been studied using a search for the radiative leptonic decay B->gamma l nu at BaBar and shown to be superior to that of all other attempted classifiers including such powerful methods as boosted decision trees. In the B->gamma e nu channel, the described algorithm increases the expected signal significance from 2.4 sigma obtained by an original method designed for the B->gamma l nu analysis to 3.0 sigma.
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