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.

• Type: Conference
• Year: 2005
• Where: RSFDGRC
• Authors: Yuan Jiang, Jinjiang Ling, Gang Li, Honghua Dai, Zhi-Hua Zhou
• Keywords: Algorithm Obtains Bootstrap, Directed Acyclic Graph, Nearest Neighbor Classifiers, RSFDGRC 2005