提出最优过滤顺序算法，显著降低系统成本。
Optimality of Sequential Filtering Under Independent Cost and Selectivity Models
- 按成本与拒收概率比值排序过滤器
- 模拟显示成本低于常见启发式方法
- 适合大规模系统优化决策者
序列过滤管道是大规模系统中的常见设计模式，通过一系列阶段逐步减少项目数量，每个阶段均产生成本。尽管广泛应用于排序系统、级联机器学习推理和欺诈检测中，过滤器顺序通常依赖启发式方法而无理论保证。本文在期望成本目标下形式化序列过滤问题，并证明在独立性假设下，按成本与拒绝概率的比值递增顺序排列过滤器可最小化总期望成本。大量蒙特卡洛模拟表明，该最优顺序在所有运行中严格优于常见启发式方法，无论在期望值还是整体结果分布上均表现更优。
原文摘要 · Abstract (English)
Sequential filtering pipelines are a common design pattern in large-scale systems, where a large population of items is progressively reduced by a sequence of stages that each incur cost. Despite their prevalence in ranking systems, cascaded machine learning inference, and fraud detection, filter ordering is often determined by heuristics without formal guarantees. We formalize sequential filtering under an expected-cost objective and prove that, under an independence model, ordering filters by increasing ratio of cost to rejection probability minimizes expected total cost. Extensive Monte Carlo simulations show that the optimal ordering strictly dominates common heuristics across all runs, both in expectation and across the full distribution of outcomes.
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