Popular Ensemble Methods: An Empirical Study

TL;DR

This empirical study compares Bagging and Boosting ensemble methods across 23 datasets using neural networks and decision trees, revealing their relative accuracies, overfitting tendencies, and the impact of dataset characteristics.

Contribution

It provides a comprehensive empirical evaluation of Bagging and Boosting, highlighting their performance differences and data-dependent behaviors in ensemble classification.

Findings

Bagging is generally more accurate than single classifiers.

Boosting can outperform Bagging but may overfit noisy data.

Most performance gains occur within the first few classifiers, with larger gains up to 25 classifiers for Boosting decision trees.

Abstract

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 and 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.