Many real-world multi-label prediction problems involve set-valued predictions that must satisfy specific requirements dictated by downstream usage. We focus on a typical scenario where such requirements, separately encoding \textit{value} and \textit{cost}, compete with each other. For instance, a hospital might expect a smart diagnosis system to capture as many severe, often co-morbid, diseases as possible (the value), while maintaining strict control over incorrect predictions (the cost). We present a general pipeline, dubbed as FavMac, to maximize the value while controlling the cost in such scenarios. FavMac can be combined with almost any multi-label classifier, affording distribution-free theoretical guarantees on cost control. Moreover, unlike prior works, FavMac can handle real-world large-scale applications via a carefully designed online update mechanism, which is of independent interest. Our methodological and theoretical contributions are supported by experiments on several healthcare tasks and synthetic datasets - FavMac furnishes higher value compared with several variants and baselines while maintaining strict cost control.
翻译:许多真实世界的多标签预测问题涉及必须满足下游使用要求的具体要求的定值预测。 我们侧重于一种典型的情景,即这种要求分别编码\textit{value}和\textit{cost}相互竞争。例如,医院可能期望智能诊断系统能够捕捉尽可能多的严重、往往是共同发病的疾病(价值),同时严格控制不正确的预测(成本)。我们提出了一个一般管道,称为FavMac, 以在这种情景中控制成本时实现价值最大化。 FavMac可以与几乎所有的多标签分类器相结合,在成本控制方面提供无分配的理论保证。此外,与以前的工作不同,FavMac可以通过精心设计的网上更新机制处理现实世界的大规模应用,这是一个独立感兴趣的机制。我们的方法和理论贡献得到了若干保健任务和合成数据集的实验的支持。 FavMac提供的价值高于若干变量和基准,同时保持严格的成本控制。