Popular Ensemble Methods: An Empirical Study

Popular Ensemble Methods: An Empirical Study

1999 | David Opitz, Richard Maclin
This paper evaluates the effectiveness of Bagging and Boosting ensemble methods on 23 data sets using neural networks and decision trees. The results show that Bagging generally produces more accurate classifiers than single classifiers, while Boosting can sometimes outperform Bagging but can also lead to less accurate ensembles, especially with neural networks. The performance of Boosting is found to be dependent on the characteristics of the data set, and it may overfit noisy data, leading to decreased performance. The study also indicates that most of the gain in ensemble performance comes from the first few classifiers combined, but Boosting decision trees can show significant improvements up to 25 classifiers. Additionally, a simple ensemble approach using randomly initialized neural networks often performs as well as Bagging. The paper concludes that Bagging is a reliable method for most problems, while Boosting may offer larger gains in accuracy under certain conditions.This paper evaluates the effectiveness of Bagging and Boosting ensemble methods on 23 data sets using neural networks and decision trees. The results show that Bagging generally produces more accurate classifiers than single classifiers, while Boosting can sometimes outperform Bagging but can also lead to less accurate ensembles, especially with neural networks. The performance of Boosting is found to be dependent on the characteristics of the data set, and it may overfit noisy data, leading to decreased performance. The study also indicates that most of the gain in ensemble performance comes from the first few classifiers combined, but Boosting decision trees can show significant improvements up to 25 classifiers. Additionally, a simple ensemble approach using randomly initialized neural networks often performs as well as Bagging. The paper concludes that Bagging is a reliable method for most problems, while Boosting may offer larger gains in accuracy under certain conditions.
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