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conference contributionposted 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.