  Learning a classifier with control on the false-positive 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 false-positive rate constraint. In this paper, we
propose a novel scoring-thresholding approach, tau-False Positive Learning
(tau-FPL) to address this problem. We show the scoring problem which takes the
false-positive rate tolerance into accounts can be efficiently solved in linear
time, also an out-of-bootstrap thresholding method can transform the learned
ranking function into a low false-positive classifier. Both theoretical
analysis and experimental results show superior performance of the proposed
tau-FPL over existing approaches.
