# A Policy for Early Sequence Classification

arXiv: https://arxiv.org/abs/2304.03463 · HTML: https://mlsanity.com/paper/2304.03463/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Global overview of Imitation Learning](https://mlsanity.com/paper/1801.06503/) (2018)
- [Convolutional Recurrent Neural Networks for Electrocardiogram Classification](https://mlsanity.com/paper/1710.06122/) (2017)

## Built on by (most cited first, 2 of 2)

- [Early Classification of Time Series: A Survey and Benchmark](https://mlsanity.com/paper/2406.18332/) (2024)
- [Early Classifying Multimodal Sequences](https://mlsanity.com/paper/2305.01151/) (2023)

## Abstract (arXiv)

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.