Ensemble Methods in Supervised Learning.

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


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 methods including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.
Original languageEnglish
Title of host publicationData Mining and Knowledge Discovery Handbook
PublisherSpringer, Boston, MA
Number of pages21
ISBN (Electronic)978-0-387-09823-4
ISBN (Print)978-0-387-09822-7
StatePublished - 2010


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