Bagging predictors
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BA method was proposed by Breiman et al. and is often used in BCI.
“BA method was proposed by Breiman et al. [30] and is often used in BCI [31,32].”
From Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier (2020) · cites this paper as [30]
Methods that quantify uncertainty can be broadly classified into Bayesian approaches and Frequentist approaches.
“These methods can be broadly classified into two categories: Bayesian approaches [40,90,106] and Frequentist, approaches [14,70,113]”
From Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2021) · cites this paper as [14]
The Introduction describes bagging and mentions a separate Bayesian approach, but does not classify uncertainty-quantification methods as Bayesian or Frequentist. Section 6.1 discusses class-probability estimates, not that proposed classification. Searches for uncertainty quantification and frequentist approaches found no passage addressing the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredFeature selection offers advantages including cost reduction in data acquisition and classification model training, decreased model size, enhanced classification performance, and potentially improved interpretability.
“As discussed earlier, feature selection offers several advantages, including cost reduction in data acquisition and classification model training, decreased model size, enhanced classification performance, and potentially improved inter- [53]].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [53]
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Iterative random forests to discover predictive and stable high-order interactions
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Real-time sleep apnea detection by classifier combination
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- Authors
- Breiman, L.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Breiman, Leo
- Venue
- Machine Learning
- Published
- 1996-08
- Volume
- 24
- Issue
- 2
- Pages
- 123-140
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W4212883601
- Cited by
- 16,325
- References
- 11
- Citations by year
- 2026: 292 · 2025: 961 · 2024: 1566 · 2023: 1675 · 2022: 1623 · 2021: 1554 · 2020: 1186 · 2019: 1006 · 2018: 762 · 2017: 712 · 2016: 590 · 2015: 577 · 2014: 527 · 2013: 470 · 2012: 404
- FWCI
- 24.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- bagging, ensemble learning, prediction accuracy, classification trees, regression trees, subset selection, linear regression, plurality voting, numerical prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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