# A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping

arXiv: https://arxiv.org/abs/1409.5165 · HTML: https://mlsanity.com/paper/1409.5165/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2014.

## Built on by (most cited first, 21 of 21)

- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [Small-Text: Active Learning for Text Classification in Python](https://mlsanity.com/paper/2107.10314/) (2021)
- [Hitting the Target: Stopping Active Learning at the Cost-Based Optimum](https://mlsanity.com/paper/2110.03802/) (2021)
- [OneLabeler: A Flexible System for Building Data Labeling Tools](https://mlsanity.com/paper/2203.14227/) (2022)
- [CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials](https://mlsanity.com/paper/2201.02260/) (2022)
- [Stopping Criterion for Active Learning Based on Error Stability](https://mlsanity.com/paper/2104.01836/) (2021)
- [Active Learning for Network Traffic Classification: A Technical Study](https://mlsanity.com/paper/2106.06933/) (2021)
- [Robust online active learning](https://mlsanity.com/paper/2302.00422/) (2023)
- [Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis](https://mlsanity.com/paper/2212.11680/) (2022)
- [Active Transfer Learning for Efficient Video-Specific Human Pose Estimation](https://mlsanity.com/paper/2311.05041/) (2023)
- [Finding Needles in the Haystack: Transductive Active Labeling in Ecology](https://mlsanity.com/paper/2606.03821/) (2026)
- [Impact of Stop Sets on Stopping Active Learning for Text Classification](https://mlsanity.com/paper/2201.05460/) (2022)
- [ALANNO: An Active Learning Annotation System for Mortals](https://mlsanity.com/paper/2211.06224/) (2022)
- [Early stopping by correlating online indicators in neural networks](https://mlsanity.com/paper/2402.02513/) (2024)
- [Modeling of learning curves with applications to pos tagging](https://mlsanity.com/paper/2402.02515/) (2024)
- [Adaptive scheduling for adaptive sampling in POS taggers construction](https://mlsanity.com/paper/2402.02516/) (2024)
- [Absolute convergence and error thresholds in non-active adaptive sampling](https://mlsanity.com/paper/2402.02522/) (2024)
- [An (ε,δ)-accurate level set estimation with a stopping criterion](https://mlsanity.com/paper/2503.20272/) (2025)
- [Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes](https://mlsanity.com/paper/2607.05464/) (2026)
- [How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification](https://mlsanity.com/paper/2608.03511/) (2026)
- [Active Inference as Context Acquisition for AI Agents](https://mlsanity.com/paper/2608.19202/) (2026)

## Abstract (arXiv)

A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annotation/performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) attention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior.