Author

Chen-An Tai

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Preprint Aug 2026

FETERS: Few-Shot Early Time-Series Classification via Effective Ratio Selection

FETERS is proposed, a few-shot ETSC framework that selects a dataset-level stopping ratio through class-wise leave-one-out (LOO) evaluation on the support set and uses a penalty-based reward function to manage the accuracy-earliness trade-off, thereby avoiding the need to train an additional stopping module.

Chen-An Tai, Yujia Wu, Vincent S. Tseng · 0 citations