用于优化HiggsML挑战中发现重要性的加权分类级联
Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge
摘要：
我们介绍了一个minorization-maximization方法来优化高能物理中发现意义的常见措施。该方法在解决加权二进制分类问题和以简单的封闭形式更新类权重之间交替。此外，一个参数的基础上凸对偶表明，在任何一轮加权分类错误的改善产生了相称的改善发现的意义。我们用2014年希格斯玻色子机器学习挑战赛的实验结果来补充我们的推导。
英文摘要：
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.
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