# Consistent optimization of AMS by logistic loss minimization

@article{Kotlowski2014ConsistentOO, title={Consistent optimization of AMS by logistic loss minimization}, author={Wojciech Kotlowski}, journal={ArXiv}, year={2014}, volume={abs/1412.2106} }

In this paper, we theoretically justify an approach popular among participants of the Higgs Boson Machine Learning Challenge to optimize approximate median significance (AMS). The approach is based on the following two-stage procedure. First, a real-valued function is learned by minimizing a surrogate loss for binary classification, such as logistic loss, on the training sample. Then, a threshold is tuned on a separate validation sample, by direct optimization of AMS. We show that the regret of… CONTINUE READING

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