Monday, June 3, 2013

1305.7248 (Justin Stevens et al.)

uBoost: A boosting method for producing uniform selection efficiencies
from multivariate classifiers

Justin Stevens, Mike Williams
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 user-defined multivariate space. Such a technique is ideally suited for amplitude analyses or other situations where optimizing a single integrated figure of merit is not what is desired.
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