Popular Ensemble Methods: An Empirical Study

TLDR

An ensemble combines predictions from multiple classifiers, often yielding higher accuracy than individual models, with bagging and boosting being prominent techniques. The study evaluates bagging and boosting on 23 datasets using neural networks and decision trees. The authors applied bagging and boosting to neural networks and decision trees across 23 datasets. Bagging generally outperforms single classifiers but is often outperformed by boosting, which can underperform single models on neural networks, overfit noisy data, and yields most performance gains in the first few classifiers, though up to 25 can still improve 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 & 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.