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Hitting the Target: Stopping Active Learning at the Cost-Based Optimum [article]

Zac Pullar-Strecker, Katharina Dost, Eibe Frank, Jörg Wicker
2021 arXiv   pre-print
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]

M. Moustapha, S. Marelli, B. Sudret
2021 arXiv   pre-print
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]

Michael Bloodgood, K. Vijay-Shanker
2014 arXiv   pre-print
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

Yin Chu Yeh, Wenyuan Li, Adriel Lau, Ke Wang
2013 IEEE Transactions on Power Systems  
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

Gregor Wiedemann
2019 Figshare  
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

Musa Mailah, Wun Shiung Jonathan Chong
2002 Jurnal Teknologi  
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

Tanay Kumar Saha, Mohammad Al Hasan, Chandler Burgess, Md Ahsan Habib, Jeff Johnson
2015 2015 IEEE International Conference on Big Data (Big Data)  
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]

Michael Bloodgood, K. Vijay-Shanker
2014 arXiv   pre-print
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]

Hideaki Ishibashi, Hideitsu Hino
2020 arXiv   pre-print
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]

Divya Shanmugam, Samira Shabanian, Fernando Diaz, Michèle Finck, Asia Biega
2021 arXiv   pre-print
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

Alain Rakotomamonjy, Sokol Koco, Liva Ralaivola
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
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]

Hideaki Ishibashi, Hideitsu Hino
2021 arXiv   pre-print
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  
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

Michael Altschuler, Michael Bloodgood
2019 2019 IEEE 13th International Conference on Semantic Computing (ICSC)  
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

Garrett Beatty, Ethan Kochis, Michael Bloodgood
2019 2019 IEEE 13th International Conference on Semantic Computing (ICSC)  
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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