Who Cited It

Random Forests

2001 · Machine Learning · 131,109 citations · 45 from inside this corpus

Leo Breiman

No abstract in the source record.

Random Forests (2001)Random ForestsBagging Predictors (1996)Bagging PredictorsExperiments with a new boosting algorithm (1996)Experiments with a new boosti…The random subspace method for constructing decision forests (1998)The random subspace method fo…An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees:… (2000)An Experimental Comparison of…An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia… (1999)An Empirical Comparison of Vo…Boosting the margin: a new explanation for the effectiveness of voting methods (1998)Boosting the margin: a new ex…XGBoost (2016)XGBoostExtremely randomized trees (2006)Extremely randomized treesImproving neural networks by preventing co-adaptation of feature detectors (2012)Improving neural networks by …The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i… (2020)The advantages of the Matthew…Feature Selection with the Boruta Package (2010)Feature Selection with the Bo…Machine Learning: Algorithms, Real-World Applications and Research Directions (2021)Machine Learning: Algorithms,…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) (2001)Statistical Modeling: The Two…A random forest guided tour (2016)A random forest guided tourGradient boosting machines, a tutorial (2013)Gradient boosting machines, a…Machine Learning in Medicine (2015)Machine Learning in MedicineDiagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases … (2017)Diagnostic Assessment of Deep…Ensemble learning: A survey (2018)Ensemble learning: A surveyUnderstanding Machine Learning: From Theory To Algorithms (2015)Understanding Machine Learnin…Ensemble based systems in decision making (2006)Ensemble based systems in dec…A comparative analysis of gradient boosting algorithms (2020)A comparative analysis of gra…Variable selection using random forests (2010)Variable selection using rand…Exploratory Undersampling for Class-Imbalance Learning (2008)Exploratory Undersampling for…Definitions, methods, and applications in interpretable machine learning (2019)Definitions, methods, and app…Ensemble deep learning: A review (2022)Ensemble deep learning: A rev…Model compression (2006)Model compressionIsolation-Based Anomaly Detection (2012)Isolation-Based Anomaly Detec…On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation (2010)On Over-fitting in Model Sele…Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023)Interpreting Black-Box Models…A survey on ensemble learning (2019)A survey on ensemble learningBroad Learning System: An Effective and Efficient Incremental Learning System Without the… (2017)Broad Learning System: An Eff…Multi-class AdaBoost (2009)Multi-class AdaBoostRotation Forest: A New Classifier Ensemble Method (2006)Rotation Forest: A New Classi…Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual C… (2014)Peeking Inside the Black Box:…A Dataset for Breast Cancer Histopathological Image Classification (2015)A Dataset for Breast Cancer H…
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What this paper cites, inside the corpus

PaperYearCited
Bagging Predictors199617,191
Experiments with a new boosting algorithm19967,582
The random subspace method for constructing decision forests19986,904
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees:…20002,962
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…19992,656
Boosting the margin: a new explanation for the effectiveness of voting methods19982,344

What cites it, inside the corpus

PaperYearCited
XGBoost201652,304
Extremely randomized trees20069,068
Improving neural networks by preventing co-adaptation of feature detectors20126,653
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…20206,132
Feature Selection with the Boruta Package20105,387
Machine Learning: Algorithms, Real-World Applications and Research Directions20215,368
Learning Deep Architectures for AI20095,077
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)20014,362
A random forest guided tour20164,026
Gradient boosting machines, a tutorial20133,896
Machine Learning in Medicine20153,534
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases …20173,388
Ensemble learning: A survey20183,088
Understanding Machine Learning: From Theory To Algorithms20153,081
Ensemble based systems in decision making20062,965
A comparative analysis of gradient boosting algorithms20202,692
Variable selection using random forests20102,615
Exploratory Undersampling for Class-Imbalance Learning20082,504
Definitions, methods, and applications in interpretable machine learning20192,161
Ensemble deep learning: A review20222,136
Model compression20062,118
Isolation-Based Anomaly Detection20122,055
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation20101,988
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence20231,981
A survey on ensemble learning20191,951
Broad Learning System: An Effective and Efficient Incremental Learning System Without the…20171,936
Multi-class AdaBoost20091,916
Rotation Forest: A New Classifier Ensemble Method20061,911
Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual C…20141,838
A Dataset for Breast Cancer Histopathological Image Classification20151,822

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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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  • supports1 author record(s) attached.
  • supports16 reference(s) recorded.
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  • supportsA title is present.

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