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On the size of training set and the benefit from ensemble
journal contribution
posted on 2004-01-01, 00:00 authored by Z H Zhou, D Wei, Gang LiGang Li, Honghua DaiIn this paper, the impact of the size of the training set on the benefit from ensemble, i.e. the gains obtained by employing ensemble learning paradigms, is empirically studied. Experiments on Bagged/ Boosted J4.8 decision trees with/without pruning show that enlarging the training set tends to improve the benefit from Boosting but does not significantly impact the benefit from Bagging. This phenomenon is then explained from the view of bias-variance reduction. Moreover, it is shown that even for Boosting, the benefit does not always increase consistently along with the increase of the training set size since single learners sometimes may learn relatively more from additional training data that are randomly provided than ensembles do. Furthermore, it is observed that the benefit from ensemble of unpruned decision trees is usually bigger than that from ensemble of pruned decision trees. This phenomenon is then explained from the view of error-ambiguity balance.
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Journal
Lecture notes in computer scienceVolume
3056Pagination
298 - 307Publisher
Springer-VerlagLocation
Heidelberg, GermanyISSN
0302-9743eISSN
1611-3349Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2004, Springer-VerlagUsage metrics
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