uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers
Author(s)
Stevens, Justin; Williams, Michael
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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.
Date issued
2013-12Department
Massachusetts Institute of Technology. Department of Physics; Massachusetts Institute of Technology. Laboratory for Nuclear ScienceJournal
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
ISSN
1748-0221