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Dependency bagging
conference contribution
posted on 2005-01-01, 00:00 authored by Y Jiang, J J Ling, Gang LiGang Li, H 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. © Springer-Verlag Berlin Heidelberg 2005.
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Volume
3641 LNAIPagination
491 - 500Publisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783540286530ISBN-10
3540286535Publication classification
E1.1 Full written paper - refereedTitle of proceedings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Usage metrics
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