Ensemble Methods for Classifiers.

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationThe Data Mining and Knowledge Discovery Handbook
EditorsLior Rokach , O. Maimon
PublisherSpringer, Boston, MA
Pages957-980
Number of pages24
ISBN (Electronic)978-0-387-25465-4
ISBN (Print)978-0-387-24435-8
DOIs
StatePublished - 2005

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