  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.
