Root Node Vaccines for Bayesian Network Structure Learning Based on Immune Algorithm

Abstract

To facilitate the application of Bayesian network in engineering fields, learning proper structure from dataset is one of the most efficiency Bayesian network modeling technique. In this paper, the description and characteristics of Bayesian networks and immune algorithms are discussed at first. Then, the extraction method of root node vaccines is proposed to accelerate the model structure learning process. Thirdly, the immune algorithm based method is also applied to search the best Bayesian network structure. Finally, the simulation studies based on a car start BN model are carried out and the results verify that the proposed Bayesian network structure learning method can build the objective structure from dataset more effectively and more efficiently with the root node vaccines.