  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.
