Sciweavers

RSFDGRC
2005
Springer

Dependency Bagging

14 years 3 months ago
Dependency Bagging
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.
Yuan Jiang, Jinjiang Ling, Gang Li, Honghua Dai, Z
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where RSFDGRC
Authors Yuan Jiang, Jinjiang Ling, Gang Li, Honghua Dai, Zhi-Hua Zhou
Comments (0)