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Optimal sampling in unbiased active learning

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Název: Optimal sampling in unbiased active learning
Autoři: Imberg, Henrik, 1991, Jonasson, Johan, 1966, Axelson-Fisk, Marina, 1972
Zdroj: Statistical sampling in machine learning 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Online Proceedings of Machine Learning Research. 108:559-569
Témata: Optimal design, Weighted loss, Sampling weights, Generalised linear models, Unequal probability sampling, Active learning
Popis: A common belief in unbiased active learning is that, in order to capture the most informative instances, the sampling probabilities should be proportional to the uncertainty of the class labels. We argue that this produces suboptimal predictions and present sampling schemes for unbiased pool-based active learning that minimise the actual prediction error, and demonstrate a better predictive performance than competing methods on a number of benchmark datasets. In contrast, both probabilistic and deterministic uncertainty sampling performed worse than simple random sampling on some of the datasets.
Popis souboru: electronic
Přístupová URL adresa: https://research.chalmers.se/publication/536361
https://research.chalmers.se/publication/520253
https://research.chalmers.se/publication/519957
http://proceedings.mlr.press/v108/imberg20a/imberg20a.pdf
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Items – Name: Title
  Label: Title
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  Data: Optimal sampling in unbiased active learning
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Imberg%2C+Henrik%22">Imberg, Henrik</searchLink>, 1991<br /><searchLink fieldCode="AR" term="%22Jonasson%2C+Johan%22">Jonasson, Johan</searchLink>, 1966<br /><searchLink fieldCode="AR" term="%22Axelson-Fisk%2C+Marina%22">Axelson-Fisk, Marina</searchLink>, 1972
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  Data: <i>Statistical sampling in machine learning 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Online Proceedings of Machine Learning Research</i>. 108:559-569
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  Data: <searchLink fieldCode="DE" term="%22Optimal+design%22">Optimal design</searchLink><br /><searchLink fieldCode="DE" term="%22Weighted+loss%22">Weighted loss</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+weights%22">Sampling weights</searchLink><br /><searchLink fieldCode="DE" term="%22Generalised+linear+models%22">Generalised linear models</searchLink><br /><searchLink fieldCode="DE" term="%22Unequal+probability+sampling%22">Unequal probability sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Active+learning%22">Active learning</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: A common belief in unbiased active learning is that, in order to capture the most informative instances, the sampling probabilities should be proportional to the uncertainty of the class labels. We argue that this produces suboptimal predictions and present sampling schemes for unbiased pool-based active learning that minimise the actual prediction error, and demonstrate a better predictive performance than competing methods on a number of benchmark datasets. In contrast, both probabilistic and deterministic uncertainty sampling performed worse than simple random sampling on some of the datasets.
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        StartPage: 559
    Subjects:
      – SubjectFull: Optimal design
        Type: general
      – SubjectFull: Weighted loss
        Type: general
      – SubjectFull: Sampling weights
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      – SubjectFull: Generalised linear models
        Type: general
      – SubjectFull: Unequal probability sampling
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      – SubjectFull: Active learning
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              Y: 2020
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            – TitleFull: Statistical sampling in machine learning 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Online Proceedings of Machine Learning Research
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