ERROR CORRELATION AND ERROR REDUCTION IN ENSEMBLE CLASSIFIERS
Kagan Tumer and Joydeep Ghosh
Using an ensemble of classifiers, instead of a single
classifier, can lead to improved generalization.
The gains obtained by combining however, are often affected
more by the selection of what is presented to the combiner,
than by the actual combining method that is chosen.
In this paper we focus on data selection and classifier
training methods, in order to ``prepare'' classifiers for
combining. We review a combining framework for classification
problems that quantifies the need for reducing the correlation
among individual classifiers.
Then, we discuss several methods that make the classifiers in
an ensemble more complementary. Experimental results
are provided to illustrate the benefits and pitfalls of
reducing the correlation among classifiers, especially when
the training data is in limited supply.
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