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.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v42-diaz14, title = {Optimization of AMS using Weighted AUC optimized models}, author = {Díaz-Morales, Roberto and Navia-Vázquez, Ángel}, booktitle = {Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning}, pages = {109--127}, year = {2015}, editor = {Cowan, Glen and Germain, Cécile and Guyon, Isabelle and Kégl, Balázs and Rousseau, David}, volume = {42}, series = {Proceedings of Machine Learning Research}, address = {Montreal, Canada}, month = {13 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v42/diaz14.pdf}, url = {https://proceedings.mlr.press/v42/diaz14.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Optimization of AMS using Weighted AUC optimized models %A Roberto Díaz-Morales %A Ángel Navia-Vázquez %B Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Glen Cowan %E Cécile Germain %E Isabelle Guyon %E Balázs Kégl %E David Rousseau %F pmlr-v42-diaz14 %I PMLR %P 109--127 %U https://proceedings.mlr.press/v42/diaz14.html %V 42 %X 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.
RIS
TY - CPAPER TI - Optimization of AMS using Weighted AUC optimized models AU - Roberto Díaz-Morales AU - Ángel Navia-Vázquez BT - Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning DA - 2015/08/27 ED - Glen Cowan ED - Cécile Germain ED - Isabelle Guyon ED - Balázs Kégl ED - David Rousseau ID - pmlr-v42-diaz14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 42 SP - 109 EP - 127 L1 - http://proceedings.mlr.press/v42/diaz14.pdf UR - https://proceedings.mlr.press/v42/diaz14.html AB - 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. ER -
APA
Díaz-Morales, R. & Navia-Vázquez, Á.. (2015). Optimization of AMS using Weighted AUC optimized models. Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, in Proceedings of Machine Learning Research 42:109-127 Available from https://proceedings.mlr.press/v42/diaz14.html.

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