Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge
Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:129-134, 2015.
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.