Dependency bagging

Authors:
Yuan Jiang;Jin-Jiang Ling;Gang Li;Honghua Dai;Zhi-Hua Zhou
Affiliations:
National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;School of Information Technology, Deakin University, Burwood, Vic, Australia;School of Information Technology, Deakin University, Burwood, Vic, Australia;National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

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Abstract

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.