Who Cited It

Rotation Forest: A New Classifier Ensemble Method

2006 · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1,911 citations · 4 from inside this corpus

Juan J. Rodríguez, Ludmila I. Kuncheva, Carlos J. Alonso

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Rotation Forest: A New Classifier Ensemble Method (2006)Rotation Forest: A New Classi…Random Forests (2001)Random ForestsC4.5: Programs for Machine Learning (1992)C4.5: Programs for Machine Le…Bagging Predictors (1996)Bagging PredictorsBagging predictors (1996)Bagging predictorsUCI Repository of machine learning databases (1998)UCI Repository of machine lea…Ensemble Methods in Machine Learning (2000)Ensemble Methods in Machine L…The random subspace method for constructing decision forests (1998)The random subspace method fo…Neural network ensembles (1990)Neural network ensemblesRandom forests (2024)Random forestsCombining Pattern Classifiers (2004)Combining Pattern ClassifiersAn Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia… (1999)An Empirical Comparison of Vo…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…Improved Boosting Algorithms Using Confidence-rated Predictions (1999)Improved Boosting Algorithms …Statistical Pattern Recognition (2001)Statistical Pattern Recogniti…Ensemble learning: A survey (2018)Ensemble learning: A surveyA survey on ensemble learning (2019)A survey on ensemble learningDecision trees: a recent overview (2011)Decision trees: a recent over…KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008)KEEL: a software tool to asse…
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What this paper cites, inside the corpus

PaperYearCited
Random Forests2001131,109
C4.5: Programs for Machine Learning199223,704
Bagging Predictors199617,191
Bagging predictors199616,468
UCI Repository of machine learning databases199810,546
Ensemble Methods in Machine Learning20008,058
The random subspace method for constructing decision forests19986,904
Neural network ensembles19904,288
Random forests20243,553
Combining Pattern Classifiers20042,980
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…19992,656
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
Improved Boosting Algorithms Using Confidence-rated Predictions19991,966
Statistical Pattern Recognition20011,756

What cites it, inside the corpus

PaperYearCited
Ensemble learning: A survey20183,088
A survey on ensemble learning20191,951
Decision trees: a recent overview20111,577
KEEL: a software tool to assess evolutionary algorithms for data mining problems20081,508

Links

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Topics

Neural Networks and ApplicationsComputer Science
Face and Expression RecognitionComputer Science
Machine Learning and Data ClassificationComputer Science

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complete

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  • supports3 author record(s) attached.
  • supports57 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:52+00:00.

sha256 930f5bc64fea8a60…