Affiliations: [a] College of Computer Science and Technology, Jilin University, Changchun, Jilin, China | [b] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China | [c] Department of Software and Big Data, Changzhou College of Information Technology, Changzhou, Jiangsu, China | [d] School of Foreign Languages, Changchun University of Technology, Changchun, Jilin, China
Correspondence:
[*]
Corresponding author: Limin Wang, College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China. E-mail: [email protected].
Abstract: Bagging has attracted much attention due to its simple implementation and the popularity of bootstrapping. By learning diverse classifiers from resampled datasets and averaging the outcomes, bagging investigates the possibility of achieving substantial classification performance of the base classifier. Diversity has been recognized as a very important characteristic in bagging. This paper presents an efficient and effective bagging approach, that learns a set of independent Bayesian network classifiers (BNCs) from disjoint data subspaces. The number of bits needed to describe the data is measured in terms of log likelihood, and redundant edges are identified to optimize the topologies of the learned BNCs. Our extensive experimental evaluation on 54 publicly available datasets from the UCI machine learning repository reveals that the proposed algorithm achieves a competitive classification performance compared with state-of-the-art BNCs that use or do not use bagging procedures, such as tree-augmented naive Bayes (TAN), k-dependence Bayesian classifier (KDB), bagging NB or bagging TAN.
