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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 Zhou
In 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 LNAI

Pagination

491 - 500

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783540286530

ISBN-10

3540286535

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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