Who Cited It

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

2000 · Machine Learning · 2,962 citations · 17 from inside this corpus

Thomas G. Dietterich

No abstract in the source record.

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees:… (2000)An Experimental Comparison of…Bagging Predictors (1996)Experiments with a new boosting algorithm (1996)Experiments with a new boosti…Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms (1998)Approximate Statistical Tests…An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia… (1999)An Empirical Comparison of Vo…UCI Repository of Machine Learning Databases (1996)UCI Repository of Machine Lea…Random Forests (2001)Random ForestsExtremely randomized trees (2006)Extremely randomized treesEnsemble Methods in Machine Learning (2000)Ensemble Methods in Machine L…Supervised Machine Learning: A Review of Classification Techniques (2007)Supervised Machine Learning: …Ensemble learning: A survey (2018)Ensemble learning: A surveyEnsemble based systems in decision making (2006)Ensemble based systems in dec…A Short Introduction to Boosting (1999)A Short Introduction to Boost…A comparative analysis of gradient boosting algorithms (2020)A comparative analysis of gra…Variable selection using random forests (2010)Variable selection using rand…Improved boosting algorithms using confidence-rated predictions (1998)Improved boosting algorithms …Boosting the margin: a new explanation for the effectiveness of voting methods (1998)Boosting the margin: a new ex…Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac… (2003)Measures of Diversity in Clas…The Boosting Approach to Machine Learning: An Overview (2003)The Boosting Approach to Mach…On the algorithmic implementation of multiclass kernel-based vector machines (2002)On the algorithmic implementa…Classification in the Presence of Label Noise: A Survey (2014)Classification in the Presenc…Decision trees: a recent overview (2011)Decision trees: a recent over…Machine learning: a review of classification and combining techniques (2006)Machine learning: a review of…
22 of 22 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Bagging Predictors199617,191
Experiments with a new boosting algorithm19967,582
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms19983,759
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…19992,656
UCI Repository of Machine Learning Databases19962,343

What cites it, inside the corpus

PaperYearCited
Random Forests2001131,109
Extremely randomized trees20069,068
Ensemble Methods in Machine Learning20008,058
Supervised Machine Learning: A Review of Classification Techniques20074,149
Ensemble learning: A survey20183,088
Ensemble based systems in decision making20062,965
A Short Introduction to Boosting19992,952
A comparative analysis of gradient boosting algorithms20202,692
Variable selection using random forests20102,615
Improved boosting algorithms using confidence-rated predictions19982,573
Boosting the margin: a new explanation for the effectiveness of voting methods19982,344
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac…20032,321
The Boosting Approach to Machine Learning: An Overview20032,023
On the algorithmic implementation of multiclass kernel-based vector machines20021,789
Classification in the Presence of Label Noise: A Survey20141,777
Decision trees: a recent overview20111,577
Machine learning: a review of classification and combining techniques20061,562

Links

DOI · Open access copy · OpenAlex record

Topics

Machine Learning and Data ClassificationComputer Science
Imbalanced Data Classification TechniquesComputer Science
Neural Networks and ApplicationsComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports1 author record(s) attached.
  • supports23 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:48+00:00.

sha256 46f669157f96ea35…