Dependency bagging
Data(s) |
01/01/2005
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Resumo |
In this paper, a new variant of Bagging named <i>DepenBag </i>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.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer-Verlag |
Relação |
http://dro.deakin.edu.au/eserv/DU:30008812/n20050626.pdf http://dx.doi.org/10.1007/11548669_51 |
Direitos |
2005, Springer-Verlag |
Tipo |
Journal Article |