1993
DOI: 10.1103/physrevd.47.1900
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Comparison of the use of binary decision trees and neural networks in top-quark detection

Abstract: The use of neural networks for signal vs. background discrimination in high-energy physics experiment has been investigated and has compared favorably with the efficiency of traditional kinematic cuts. Recent work in top quark identification produced a neural network that, for a given top quark mass, yielded a higher signal to background ratio in Monte Carlo simulation than a corresponding set of conventional cuts. In this article we discuss another pattern-recognition algorithm, the binary decision tree. We h… Show more

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Cited by 22 publications
(12 citation statements)
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“…We find an optimum performance with the gradient boosted decision tree classifier (BDT) which offers the advantage over neural networks that the use of variables that do not discriminate significantly between a particular signal and background, or are highly correlated with other variables, does not compromise the classifier performance. In the BDT [34,35] approach, a series of splittings of the event sample is made at a sequential set of nodes into background-like and signal-like subsample nodes. The splitting is based upon the purity of signal and background events in a given node N and its signal-like and background-like daughter nodes S and B.…”
Section: Multivariate Analysismentioning
confidence: 99%
“…We find an optimum performance with the gradient boosted decision tree classifier (BDT) which offers the advantage over neural networks that the use of variables that do not discriminate significantly between a particular signal and background, or are highly correlated with other variables, does not compromise the classifier performance. In the BDT [34,35] approach, a series of splittings of the event sample is made at a sequential set of nodes into background-like and signal-like subsample nodes. The splitting is based upon the purity of signal and background events in a given node N and its signal-like and background-like daughter nodes S and B.…”
Section: Multivariate Analysismentioning
confidence: 99%
“…The boosted decision tree (BDT) multivariate analysis technique [39][40][41] is also used to distinguish between the W signal and the background. For the BDT analysis we apply all the selection criteria described in Sec.…”
Section: The Boosted Decision Tree Analysismentioning
confidence: 99%
“…A decision tree [57,58] employs a machine-learning technique that effectively extends a simple cut-based analysis into a multivariate algorithm with a continuous discriminant output. Boosting is a process that can be used on any weak classifier (defined as any classifier that does a little better than random guessing).…”
Section: Boosted Decision Trees Analysismentioning
confidence: 99%