uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers
Nuclear Experiment
2015-06-16 v2 High Energy Physics - Experiment
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 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.
Cite
@article{arxiv.1305.7248,
title = {uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers},
author = {Justin Stevens and Mike Williams},
journal= {arXiv preprint arXiv:1305.7248},
year = {2015}
}