Optimization of AMS using Weighted AUC optimized models


Roberto Díaz-Morales, Ángel Navia-Vázquez ;
Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:109-127, 2015.


In this paper, we present an approach to deal with the maximization of the approximate median discovery significance (AMS) in high energy physics. This paper proposes the maximization of the Weighted AUC as a criterion to train different models and the subsequent creation of an ensemble that maximizes the AMS. The algorithm described in this paper was our solution for the Higgs Boson Machine Learning Challenge and we complement this paper describing the preprocessing of the dataset, the training procedure and the experimental results that our model obtained in the challenge. This approach has proven its good performance finishing in ninth place among the solutions of 1785 teams.

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