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Dependency bagging
journal contribution
posted on 2005-01-01, 00:00 authored by Y Jiang, J J Ling, Gang LiGang Li, Honghua Dai, Z H ZhouIn this paper, a new variant of Bagging named DepenBag is proposed. This algorithm obtains bootstrap samples at first. Then, it employs a causal discoverer to induce from each sample a dependency model expressed as a Directed Acyclic Graph (DAG). The attributes without connections to the class attribute in all the DAGs are then removed. Finally, a component learner is trained from each of the resulted samples to constitute the ensemble. Empirical study shows that DepenBag is effective in building ensembles of nearest neighbor classifiers.
History
Journal
Lecture notes in computer scienceVolume
3641Pagination
491 - 500Publisher
Springer-VerlagLocation
Heidelberg, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2005, Springer-VerlagUsage metrics
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