uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers
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Williams_uBoost.pdf
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Author(s)
Stevens, Justin
Williams, Michael
Date Issued
December 2013
Journal
Journal of Instrumentation
Publisher
IOP Publishing
Citation
Stevens, J, and M Williams. “uBoost: a Boosting Method for Producing Uniform Selection Efficiencies from Multivariate Classifiers.” Journal of Instrumentation 8, no. 12 (December 23, 2013): P12013–P12013.
Version
Author's final manuscript
Abstract
The use of multivariate classifiers, especially neural networks and decision trees, has become commonplace in particle physics. Typically, a series of classifiers is trained rather than just one to enhance the performance; this is known as boosting. This paper presents a novel method of boosting that produces a uniform selection efficiency in a selected multivariate space. Such a technique is well suited for amplitude analyses or other situations where optimizing a single integrated figure of merit is not what is desired.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Laboratory for Nuclear Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1088/1748-0221/8/12/p12013
