tau-FPL: Tolerance-Constrained Learning in Linear Time

TL;DR

This paper introduces tau-FPL, a novel linear-time method for learning classifiers with strict false-positive rate control, combining theoretical guarantees and practical efficiency.

Contribution

The paper presents tau-FPL, a new scoring-thresholding approach that efficiently enforces false-positive constraints in linear time, outperforming existing methods.

Findings

Achieves false-positive control with linear time complexity

Outperforms existing approaches in experimental evaluations

Provides theoretical analysis supporting method effectiveness

Abstract

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.