tau-FPL: Tolerance-Constrained Learning in Linear Time
This addresses the need for consistent false-positive rate control in machine learning applications, offering a more efficient and effective solution.
The paper tackles the problem of learning a classifier with control on the false-positive rate, proposing tau-FPL, a scoring-thresholding approach that efficiently solves the scoring problem in linear time and achieves superior performance over existing methods.
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.