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- Title
A comparative study on swarm intelligence for structure learning of Bayesian networks.
- Authors
Ji, Junzhong; Yang, Cuicui; Liu, Jiming; Liu, Jinduo; Yin, Baocai
- Abstract
A Bayesian network (BN) is an important probabilistic model in the field of artificial intelligence and a powerful formalism used to describe uncertainty in the real world. As science and technology develop, considerable data on complex systems have been acquired by various means, which presents a significant challenge regarding how to accurately and robustly learn a network structure for a complex system. To address this challenge, many BN structure learning methods based on swarm intelligence have been developed. In this study, we perform a systematic comparison of three typical methods based on ant colony optimization, artificial bee colony algorithm, and bacterial foraging optimization. First, we analyze and summarize their main characteristics from the perspective of stochastic searching. Second, we conduct thorough experimental comparisons to examine the roles of different mechanisms in each method by means of multiaspect metrics, i.e., the K2 score, structural differences, and execution time. Next, we perform further experiments to validate the robustness of different algorithms on some benchmark data sets with noise. Finally, we present the prospects and references for researchers who are engaged in learning BN networks.
- Subjects
BAYESIAN analysis; SWARM intelligence; ANT algorithms; ARTIFICIAL intelligence; STOCHASTIC processes
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2017, Vol 21, Issue 22, p6713
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-016-2223-x