Who Cited It

Popular Ensemble Methods: An Empirical Study

1999 · Journal of Artificial Intelligence Research · 2,988 citations · 4 from inside this corpus

David W. Opitz low, Richard Maclin low

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.

Popular Ensemble Methods: An Empirical Study (1999)Popular Ensemble Methods: An …C4.5: Programs for Machine Learning (1992)C4.5: Programs for Machine Le…An Introduction to the Bootstrap (1993)An Introduction to the Bootst…Bagging Predictors (1996)Bagging PredictorsLearning Internal Representations by Error Propagation (1985)Learning Internal Representat…Advances in neural information processing systems 7 (1996)Advances in neural informatio…Stacked generalization (1992)Stacked generalizationExperiments with a new boosting algorithm (1996)Experiments with a new boosti…Adaptive Mixtures of Local Experts (1991)Adaptive Mixtures of Local Ex…Neural network ensembles (1990)Neural network ensemblesThe Handbook of brain theory and neural networks (1996)The Handbook of brain theory …Neural Networks and the Bias/Variance Dilemma (1992)Neural Networks and the Bias/…The Strength of Weak Learnability (1990)The Strength of Weak Learnabi…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…Neural Network Ensembles, Cross Validation, and Active Learning (1994)Neural Network Ensembles, Cro…Ensemble learning: A survey (2018)Ensemble learning: A surveyEnsembling neural networks: Many could be better than all (2002)Ensembling neural networks: M…Classification in the Presence of Label Noise: A Survey (2014)Classification in the Presenc…Machine learning: a review of classification and combining techniques (2006)Machine learning: a review of…
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What this paper cites, inside the corpus

PaperYearCited
C4.5: Programs for Machine Learning199223,704
An Introduction to the Bootstrap199321,041
Bagging Predictors199617,191
Learning Internal Representations by Error Propagation198516,273
Advances in neural information processing systems 7199614,393
Stacked generalization19927,777
Experiments with a new boosting algorithm19967,582
Adaptive Mixtures of Local Experts19915,234
Neural network ensembles19904,288
The Handbook of brain theory and neural networks19963,915
Neural Networks and the Bias/Variance Dilemma19923,612
The Strength of Weak Learnability19903,344
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
Neural Network Ensembles, Cross Validation, and Active Learning19941,803

What cites it, inside the corpus

PaperYearCited
Ensemble learning: A survey20183,088
Ensembling neural networks: Many could be better than all20021,990
Classification in the Presence of Label Noise: A Survey20141,777
Machine learning: a review of classification and combining techniques20061,562

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Topics

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

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