Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy

Conference paper (2002)

Authors

M Skurichina ImPhys/Quantitative Imaging
LI Kuncheva External organisation
Bob Duin ImPhys/Quantitative Imaging
Research Group
ImPhys/Quantitative Imaging
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Published Date

2002

Language

Undefined/Unknown

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Research Group

ImPhys/Quantitative Imaging

Abstract

In combining classifiers, it is believed that diverse ensembles perform better than non-diverse ones. In order to test this hypothesis, we study the accuracy and diversity of ensembles obtained in bagging and boosting applied to the nearest mean classifier. In our simulation study we consider two diversity measures: the statistic and the disagreement measure. The experiments, carried out on four data sets have shown that both diversity and the accuracy of the ensembles depend on the training sample size. With exception of very small training sample sizes, both bagging and boosting are more useful when ensembles consist of diverse classifiers. However, in boosting the relationship between diversity and the efficiency of ensembles is much stronger than in bagging.