Sequences are often not received in their entirety at once, but instead, received incrementally over time, element by element. Early predictions yielding a higher benefit, one aims to classify a sequence as accurately as possible, as soon as possible, without having to wait for the last element. For this early sequence classification, we introduce our novel classifier-induced stopping. While previous methods depend on exploration during training to learn when to stop and classify, ours is a more direct, supervised approach. Our classifier-induced stopping achieves an average Pareto frontier AUC increase of 11.8% over multiple experiments.
翻译:序列常常不能一次全部接收,而是逐个逐个地逐步接收。 由于提前进行分类可获得更高的效益,因此人们的目标是尽快、准确地对序列进行分类,而不必等待最后一个元素。 为此,我们提出了一种新颖的基于分类器的停止策略。与以往的方法依赖于训练期间进行探索来学习何时停止和分类不同,我们的方法是一种更直接的监督方法。我们的分类器停止策略在多次实验中平均帕累托前沿 AUC 增加了 11.8%。