Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge
Machine Learning
2015-09-11 v5 Machine Learning
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.
Keywords
Cite
@article{arxiv.1409.2655,
title = {Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge},
author = {Lester Mackey and Jordan Bryan and Man Yue Mo},
journal= {arXiv preprint arXiv:1409.2655},
year = {2015}
}