Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge

Lester Mackey; Jordan Bryan; Man Yue Mo

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arXiv · 1409.2655

Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge

Lester Mackey·Jordan Bryan·Man Yue Mo

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Abstract

We introduce a minorization-maximization approach to optimizing common measures of discovery significance in high energy physics. The approach alternates between solving a weighted binary classification problem and updating class weights in a simple, closed-form manner. Moreover, an argument based on convex duality shows that an improvement in weighted classification error on any round yields a commensurate improvement in discovery significance. We complement our derivation with experimental results from the 2014 Higgs boson machine learning challenge.

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Lester Mackey, Jordan Bryan, Man Yue Mo. 2014-09-09. Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge. https://arxiv.org/abs/1409.2655

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