Ensemble Methods in Supervised Learning

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...

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Bibliographic Details
Published inData Mining and Knowledge Discovery Handbook pp. 959 - 979
Main Author Rokach, Lior
Format Book Chapter
LanguageEnglish
Published Boston, MA Springer US 2010
Subjects
AdaBoost
Arbiter Tree
Bagging
Boosting
Combiner Tree
Ensemble
Grading
Windowing
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ISBN9780387098227
0387098224
DOI10.1007/978-0-387-09823-4_50

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Summary: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.
ISBN:9780387098227
0387098224
DOI:10.1007/978-0-387-09823-4_50