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Brieman's (1996) bagging and Freund and Schapire's
(1996)boosting are recent resampling approaches to
improvingpredictive accuracy of classification rules. Both
methods combine multiple versions of unstable classifierssuch as
classification trees to a composite classifier. In this paper,
we study the applications of both techniquesto two tree-
structured methods on a collection of datasets.The results show
that, on average, both approaches can substantially improve
predictive accuracy. But on some datasets consisting of
influential observations, inferior results are obtained.A
detection rule for influential points is then proposedon the
basis of boosting algorithm. By removing influential
observationsfrom the original learning sample, our results
indicate thatbagging or boosting predictive accuracy.
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