%0 Journal Article
%T Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge
%A Lester Mackey
%A Jordan Bryan
%A Man Yue Mo
%J Computer Science
%D 2014
%I arXiv
%X 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.
%U http://arxiv.org/abs/1409.2655v5