Weighted Classification Cascades for Optimizing Discovery Significance   in the HiggsML Challenge
  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. 
Lester Mackey and Jordan Bryan and Man Yue Mo
stat.ML cs.LG