Abstract
Decomposition hybrid structure learning algorithms (DHSLAs), which combine the idea of divide and conquer with hybrid algorithms to reduce the computational complexity, are used to learn Bayesian network (BN) structure. Nevertheless, it’s hard to learn highly accurate BN structures using DHSLAs based on data alone in some cases. First, accurate divisions for the whole domain are difficult to obtain because of the effect on network density and substructures tend to be poorly learned because of the large search space. In addition, using data alone, it is difficult to distinguish Markov equivalence classes. At this point, utilizing expert knowledge is an effective way. However, existing algorithms have not been studied to integrate expert knowledge into DHSLAs. Therefore, in this paper, we propose the first structure learning algorithm for using expert knowledge in DHSLAs called Decomposition Hybrid Structure Learning Algorithm with Expert Knowledge (DHSLA-EK). In the DHSLA-EK, we incorporate domain knowledge and structural knowledge with confidence into the DHSLA by constructing prior subdomains in the decomposition stage and by forming a novel scoring function in the subdomain structure learning stage. Extensive experiments on four benchmark networks indicate that the proposed algorithm can effectively improve the learning effect of the DHSLA.
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This study was supported by the National Natural Science Foundation of China (61973067).
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Guo, H., Li, H. A decomposition hybrid structure learning algorithm for Bayesian network using expert knowledge. Knowl Inf Syst 65, 3023–3044 (2023). https://doi.org/10.1007/s10115-023-01843-4
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DOI: https://doi.org/10.1007/s10115-023-01843-4