Encyclopedia of Complexity and Systems Science

2009 Edition
| Editors: Robert A. Meyers (Editor-in-Chief)

Machine Learning, Ensemble Methods in

  • Sašo Džeroski
  • Panče Panov
  • Bernard Ženko
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30440-3_315

Definition of the Subject

Ensemble methods are machine learning methods that construct a set of predictive models and combine their outputs into a single prediction. The purpose of combining several models together is to achieve better predictive performance, and it has been shown in a number of cases that ensembles can be more accurate than single models. While some work on ensemble methods has already been done in the 1970s, it was not until the 1990s, and the introduction of methods such as bagging and boosting, that ensemble methods started to be more widely used. Today, they represent a standard machine learning method which has to be considered whenever good predictive accuracy is demanded.

Introduction

Most machine learning techniques deal with the problem of learning predictive models of data. The data are usually given as a set of examples where examples represent objects or measurements. Each example can be described in terms of values of several (independent) variables,...

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Books and Reviews

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    Brown G Ensemble learning bibliography. http://www.cs.man.ac.uk/%7Egbrown/ensemblebib/index.php. Accessed 26 March 2008
  2. 46.
    Weka 3: Data mining software in Java. http://www.cs.waikato.ac.nz/ml/weka/. Accessed 26 March 2008

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Sašo Džeroski
    • 1
  • Panče Panov
    • 1
  • Bernard Ženko
    • 1
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia