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Hitting the Target: Stopping Active Learning at the Cost-Based Optimum
[article]
2021
arXiv
pre-print
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Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional fully supervised learning. ...
framework for evaluating stopping criteria. ...
In order for active learning to provide value, these costs need to be considered when choosing a stopping criterion. ...
arXiv:2110.03802v1
fatcat:gr5mbqizlvegpgmbzj6sdqvvx4
A generalized framework for active learning reliability: survey and benchmark
[article]
2021
arXiv
pre-print
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algorithm, learning function and stopping criterion. ...
We then propose a generalized modular framework to build on-the-fly efficient active learning strategies by combining the following four ingredients or modules: surrogate model, reliability estimation ...
Stopping criterion The stopping criterion is an often overlooked yet crucial part of any active learning reliability algorithm. ...
arXiv:2106.01713v1
fatcat:f6g76j6z7jhmxmhcixoxaki7ae
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
[article]
2014
arXiv
pre-print
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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. ...
A Method for Stopping Active Learning Based on Stabilizing Predictions To stop active learning at the point when annotations stop providing increases in performance, perhaps the most straightforward way ...
arXiv:1409.5165v1
fatcat:pycqb3gjijejdh5gys56eiq3ci
Identifying PCB Contaminated Transformers Through Active Learning
2013
IEEE Transactions on Power Systems
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In this thesis, we propose a dynamic sampling size algorithm to address two key issues in active learning: the sampling size per iteration and the stopping criterion. ...
For the first time, we apply an iterative machine learning technique known as active learning to construct a PCB transformer identification model that aims to minimize the number of transformers sampled ...
[33] proposed a stopping criterion for query-by-committee based active learners. ...
doi:10.1109/tpwrs.2013.2272016
fatcat:yvhhbqgwlfeitih5jndo3mqhlm
Online_Appendix - Proportional Classification Revisited: Automatic Content Analysis of Political Manifestos Using Active Learning
2019
Figshare
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Online_Appendix for Proportional Classification Revisited: Automatic Content Analysis of Political Manifestos Using Active Learning by Gregor Wiedemann in Social Science Computer Review ...
learning as algorithm Batch-mode active learning
Input:
Initial labeled set
Set of unlabeled sentences
Batch size
Querying function
Stopping criterion
Output:
High-quality training set with × ...
}
Table A . 4 : A4 Performance of active learning with different longevity windows as stopping criterion (average of 100 repeated runs) Code Window Iteration
+
−
RMDS kappa
improvement
504 ...
doi:10.25384/sage.7932350.v1
fatcat:hhaab5ejdfbgrjkxrlexqsfiom
Control of a Robot Arm Using Iterative Learning Algorithm with a Stopping Criterion
2002
Jurnal Teknologi
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The study introduces the Active Force Control and Iterative Learning Algorithm (AFCAIL) scheme with an improved feature in the form of a suitably designed stopping criterion incorporated in the control ...
The proposed stopping criterion is specifically designed to halt the iterative learning process when the conditions related to the accuracy of the performed tasks and the acquisition of appropriate estimated ...
ACKNOWLEDGEMENTS The authors would like to thank the Ministry of Science and Technology and the Universiti Teknologi Malaysia for providing the financial support and facilities. ...
doi:10.11113/jt.v37.522
fatcat:xfaam5lsjzftzldamfaih53tum
Batch-mode active learning for technology-assisted review
2015
2015 IEEE International Conference on Big Data (Big Data)
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We also propose methods for determining the stabilization of the active learning method. ...
Additionally, these efforts have not led to a principled approach for determining the stabilization of the active learning process. ...
To the best of our knowledge, we are the first to apply stopping criterion for analyzing active learning models in the legal domain.
D. ...
doi:10.1109/bigdata.2015.7363867
dblp:conf/bigdataconf/SahaHBHJ15
fatcat:6rqn6nunuvhblaplhtdchlwmje
An Approach to Reducing Annotation Costs for BioNLP
[article]
2014
arXiv
pre-print
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There is a broad range of BioNLP tasks for which active learning (AL) can significantly reduce annotation costs and a specific AL algorithm we have developed is particularly effective in reducing annotation ...
costs for these tasks. ...
We have previously developed a stopping criterion called staticPredictions that is based on stopping when we detect that the predictions of our models on some unlabeled data have stabilized. ...
arXiv:1409.3881v1
fatcat:7oo5idr6gzebxfgpjuesnfcdbu
Stopping criterion for active learning based on deterministic generalization bounds
[article]
2020
arXiv
pre-print
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We combine the upper bound with a statistical test to derive a stopping criterion for active learning. ...
In active learning, determining the timing at which learning should be stopped is a critical issue. In this study, we propose a criterion for automatically stopping active learning. ...
Proposed method for stopping active learning We describe a specific algorithm for stopping active learning with a GP. ...
arXiv:2005.07402v1
fatcat:qawaqwzq2nfsdhpxmcfzka7tsa
Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice
[article]
2021
arXiv
pre-print
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We formalize a data minimization criterion based on performance curve derivatives and provide an effective and interpretable piecewise power law technique that models distinct stages of an algorithm's ...
In this paper, we build on literature in machine learning and law to propose the first learning framework for limiting data collection based on an interpretation that ties the data collection purpose to ...
We formalize these implications into a formal stopping criterion based on an empirical derivative of the the learned performance curve. ...
arXiv:2107.08096v1
fatcat:sti4dmldxvg6bfcgkvhgd4q5tq
More efficient sparsity-inducing algorithms using inexact gradient
2015
2015 23rd European Signal Processing Conference (EUSIPCO)
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Results on toy datasets show that inexact gradients can be as useful as exact ones provided the appropriate stopping criterion is used. ...
Our contributions are two-fold: first, we propose methodologies for computing fair estimations of inexact gradients, second we propose novel stopping criteria for computing these gradients. ...
However, they should be used in conjunction with the stability stopping criterion. ...
doi:10.1109/eusipco.2015.7362475
dblp:conf/eusipco/RakotomamonjyKR15
fatcat:rak3fbbwqzhwzkzaexjag5bxei
Stopping Criterion for Active Learning Based on Error Stability
[article]
2021
arXiv
pre-print
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We demonstrate that the proposed criterion stops active learning at the appropriate timing for various learning models and real datasets. ...
To realize efficient active learning, both an acquisition function that determines the next datum and a stopping criterion that determines when to stop learning should be considered. ...
Conclusion In this study, we proposed a stopping criterion for active learning based on error stability. ...
arXiv:2104.01836v2
fatcat:aqqkguakurbjfiog4tigju5rna
Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets
2002
Studies in Regional Science
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This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms with a view to analys ing and forecasting ...
statistical econometric learning technique for regional labour market analysis. ...
The authors also wish to thank two anonymous referees for the useful comments.
References Blien U., A. Tassinopoulos (1999), Forecasting Regional Employment with the Entrop Method, Paper ...
doi:10.2457/srs.33.3_205
fatcat:qhtrf7ll7venbaptygo6dotloi
Stopping Active Learning Based on Predicted Change of F Measure for Text Classification
2019
2019 IEEE 13th International Conference on Semantic Computing (ICSC)
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2019 pre-print arXiv:1901.09118v1 fatcat:foxqi2n2xzdnhjudjuznonex5yOther Versions
During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. ...
This stopping method can be applied with any base learner. This method is useful for reducing the data annotation bottleneck encountered when building text classification systems. ...
Stopping Method A stopping method essentially tells the model when to end the process of active learning. ...
doi:10.1109/icosc.2019.8665646
dblp:conf/semco/AltschulerB19
fatcat:2h5h4deiabanfmfdislfi7iebu
The Use of Unlabeled Data Versus Labeled Data for Stopping Active Learning for Text Classification
2019
2019 IEEE 13th International Conference on Semantic Computing (ICSC)
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2019 pre-print arXiv:1901.09126v1 fatcat:gkumyfzgvbd2zey6ieinjn44giOther Versions
Three potential sources for informing when to stop active learning are an additional labeled set of data, an unlabeled set of data, and the training data that is labeled during the process of active learning ...
Active learning is a commonly used technique to reduce the amount of training data one needs to label. A crucial aspect of active learning is determining when to stop labeling data. ...
The active learning process is shown in Algorithm 1. An important aspect of the active learning process is the stopping criterion as shown in Algorithm 1. ...
doi:10.1109/icosc.2019.8665546
dblp:conf/semco/BeattyKB19
fatcat:hcf2qg2kbjfh7iuvpy4ciqcs2y
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