Dependency bagging

Jiang, Yuan, Ling, Jin-Jiang, Li, Gang, Dai, Honghua and Zhou, Zhi-Hua 2005, Dependency bagging, Lecture notes in computer science, vol. 3641, pp. 491-500.

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Title Dependency bagging
Author(s) Jiang, Yuan
Ling, Jin-Jiang
Li, Gang
Dai, Honghua
Zhou, Zhi-Hua
Journal name Lecture notes in computer science
Volume number 3641
Start page 491
End page 500
Publisher Springer-Verlag
Place of publication Heidelberg, Germany
Publication date 2005
ISSN 0302-9743
1611-3349
Summary 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.
Language eng
Field of Research 080299 Computation Theory and Mathematics not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30008812

Document type: Journal Article
Collection: School of Information Technology
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