Lior Rokach

Senior Academic

Ensemble Methods for Classifiers.

The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. In this chapter we provide an overview of ensemble methods in classification tasks. We present all important types of ensemble method including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.
Publication language English
Pages 957-980
Publication status Published - 2005

Keywords

Ensemble
Boosting
AdaBoost
Windowing
Bagging
Grading
Arbiter Tree
Combiner Tree
Access to Document
10.1007/0-387-25465-X_45