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Ensemble Data Mining MethodsEnsemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.
Document ID
20060015642
Document Type
Book
Authors
Oza, Nikunj C. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2004
Subject Category
Documentation and Information Science
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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