tauFPL: ToleranceConstrained Learning in Linear Time
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tauFPL: ToleranceConstrained Learning in Linear Time
Learning a classifier with control on the falsepositive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the falsepositive rate constraint. In this paper, we propose a novel scoringthresholding approach, tauFalse Positive Learning (tauFPL) to address this problem. We show the scoring problem which takes the falsepositive rate tolerance into accounts can be efficiently solved in linear time, also an outofbootstrap thresholding method can transform the learned ranking function into a low falsepositive classifier. Both theoretical analysis and experimental results show superior performance of the proposed tauFPL over existing approaches.
tauFPL: ToleranceConstrained Learning in Linear Time
by Ao Zhang, Nan Li, Jian Pu, Jun Wang, Junchi Yan, Hongyuan Zha
https://arxiv.org/pdf/1801.04701v1.pdf
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