An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

1999 · Machine Learning · 2,656 citations · 20 from inside this corpus

No abstract in the source record.

34 of 34 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

Paper	Year	Cited
C4.5: Programs for Machine Learning	1992	23,704
An Introduction to the Bootstrap	1993	21,041
Bagging Predictors	1996	17,191
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection	1995	10,743
UCI Repository of machine learning databases	1998	10,546
Stacked generalization	1992	7,777
Experiments with a new boosting algorithm	1996	7,582
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms	1998	3,759
Neural Networks and the Bias/Variance Dilemma	1992	3,612
The Strength of Weak Learnability	1990	3,344
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss	1997	3,099
Boosting the margin: a new explanation for the effectiveness of voting methods	1998	2,344
Bayesian Theory	1994	2,168
Boosting a Weak Learning Algorithm by Majority	1995	1,691

What cites it, inside the corpus

Paper	Year	Cited
Random Forests	2001	131,109
Extremely randomized trees	2006	9,068
Ensemble Methods in Machine Learning	2000	8,058
A study of the behavior of several methods for balancing machine learning training data	2004	4,222
A few useful things to know about machine learning	2012	3,338
Ensemble learning: A survey	2018	3,088
Popular Ensemble Methods: An Empirical Study	1999	2,988
Ensemble based systems in decision making	2006	2,965
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees:…	2000	2,962
A Short Introduction to Boosting	1999	2,952
Improved boosting algorithms using confidence-rated predictions	1998	2,573
Exploratory Undersampling for Class-Imbalance Learning	2008	2,504
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac…	2003	2,321
The Boosting Approach to Machine Learning: An Overview	2003	2,023
Ensembling neural networks: Many could be better than all	2002	1,990
Improved Boosting Algorithms Using Confidence-rated Predictions	1999	1,966
Rotation Forest: A New Classifier Ensemble Method	2006	1,911
The foundations of cost-sensitive learning	2001	1,837
Machine learning: a review of classification and combining techniques	2006	1,562
Cost-sensitive boosting for classification of imbalanced data	2007	1,438

Links

DOI · Open access copy · OpenAlex record

Topics

Machine Learning and Data Classification	Computer Science
Advanced Statistical Methods and Models	Mathematics
Imbalanced Data Classification Techniques	Computer Science

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complete

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supports2 author record(s) attached.
supports66 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

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