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New approaches for boosting to uniformity · Around

2014

New approaches for boosting to uniformity

Rogozhnikov, Alex, Bukva, Aleksandar, Gligorov, Vladimir et al.

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The use of multivariate classifiers has become commonplace in particle physics.

To enhance the performance, a series of classifiers is typically trained; this is a technique known as boosting.
This paper explores several novel boosting methods that have been designed to produce a uniform selection efficiency in a chosen multivariate space.
Such algorithms have a wide range of applications in particle physics, from producing uniform signal selection efficiency across a Dalitz-plot to avoiding the creation of false signal peaks in an invariant mass distribution when searching for new particles.

Built on

Y. Freund, and R. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting , Journal of Computer and System Sciences 55(1)
1997
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Jerome H. Friedman, Greedy Function Approximation: A Gradient Boosting Machine , \href
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D.J. Lange, The EvtGen particle decay simulation package , \href
2001
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S. Agostinelli et al
2003
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Then

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Original
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V. V. Gligorov and M. Williams, Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree
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J. Stevens and M. Williams, uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers
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