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

arXiv 1409.5165 ↗First posted 17 Sep 2014cs.LG cs.CL stat.MLcited by 21

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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.

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

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Citing papers per year: 2021: 4; 2022: 6; 2023: 2; 2024: 4; 2025: 1; 2026: 4.

21 papers.

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  1. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  2. 2021Small-Text: Active Learning for Text Classification in Pythoncited by 19
  3. 2021Hitting the Target: Stopping Active Learning at the Cost-Based Optimumcited by 9
  4. 2022OneLabeler: A Flexible System for Building Data Labeling Toolscited by 9
  5. 2022CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materialscited by 8
  6. 2021Stopping Criterion for Active Learning Based on Error Stabilitycited by 6
  7. 2021Active Learning for Network Traffic Classification: A Technical Studycited by 5
  8. 2023Robust online active learningcited by 5
  9. 2022Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysiscited by 4
  10. 2023Active Transfer Learning for Efficient Video-Specific Human Pose Estimationcited by 4
  11. 2026Finding Needles in the Haystack: Transductive Active Labeling in Ecologycited by 3
  12. 2022Impact of Stop Sets on Stopping Active Learning for Text Classificationcited by 2
9 more
  1. 2022ALANNO: An Active Learning Annotation System for Mortalscited by 1
  2. 2024Early stopping by correlating online indicators in neural networkscited by 0
  3. 2024Modeling of learning curves with applications to pos taggingcited by 0
  4. 2024Adaptive scheduling for adaptive sampling in POS taggers constructioncited by 0
  5. 2024Absolute convergence and error thresholds in non-active adaptive samplingcited by 0
  6. 2025An (ε,δ)-accurate level set estimation with a stopping criterioncited by 0
  7. 2026Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributescited by 0
  8. 2026How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classificationcited by 0
  9. 2026Active Inference as Context Acquisition for AI Agentscited by 0