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Cited by 16,325Machine Learning and Data ClassificationImbalanced Data Classification TechniquesStatistical Methods and Inference

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Bagging predictors

Open access
Breiman, Leo·1996·Machine Learning
DOI 10.1007/bf00058655
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Registry

Crossref · 10.1007/bf00058655

Cites

12 of 12 references verified0 citation errors · 0 not checkable

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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 measured

  • Feature 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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Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. Breiman, L. (1994). Heuristics of instability in model selection, Technical Report, Statistics Department, University of California at Berkeley (to appear, Annals of Statistics).
  2. Dietterich, T.G. & Bakiri, G. (1991). Error-correcting output codes: A general method for improving multiclass inductive learning programs, Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Anaheim, CA: AAAI Press.
  3. Heath, D., Kasif, S., & Salzberg, S. (1993). k-dt: a multi-tree learning method. Proceedings of the Second International Workshop on Multistrategy Learning, 1002?1007, Chambery, France, Morgan Kaufman.

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Metadata

Authors
Breiman, L.
Affiliations
University of California, Berkeley (US)
Corresponding author
Breiman, Leo
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
1996-08
Volume
24
Issue
2
Pages
123-140
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1007/bf00058655
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
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

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