Bagging, Boosting and the Random Subspace Method for Linear Classifiers

M Skurichina, RPW Duin

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    Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually applied to, decision trees. In this paper, in contrast to a common opinion, we demonstrate that they may also be useful in linear discriminant analysis. Simulation studies, carried out for several artificial and real data sets, show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for critical training sample sizes. Finally, a table describing the possible usefulness of the combining techniques for linear classifiers is presented. Key words: Bagging; Boosting; Combining classifiers; Linear classifiers; Random subspaces; Training sample size
    Original languageUndefined/Unknown
    Pages (from-to)121-135
    Number of pages15
    JournalPattern Analysis and Applications
    Issue number2
    Publication statusPublished - 2002

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