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A decomposition hybrid structure learning algorithm for Bayesian network using expert knowledge

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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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References

  1. Alonso J, Ossa L, Gámez J, Puerta J (2018) On the use of local search heuristics to improve GES-based Bayesian network learning. Appl Soft Comput 64:366–376. https://doi.org/10.1016/j.asoc.2017.12.011

    Article  Google Scholar 

  2. Amirkhani H, Rahmati M, Lucas PJF, Hommersom A (2017) Exploiting experts’ knowledge for structure learning of Bayesian networks. IEEE T Pattern Anal 39:2154–2170. https://doi.org/10.1109/TPAMI.2016.2636828

    Article  Google Scholar 

  3. Behjati S, Beigy H (2020) Improved K2 algorithm for Bayesian network structure learning. Eng Appl Artif Intel 103617:1–12. https://doi.org/10.1016/j.engappai.2020.103617

    Article  Google Scholar 

  4. Buntine W (1991) Theory Refinement on Bayesian Networks. In: Proceedings of the Seventh Conference, Morgan Kaufmann, pp 52–60

  5. Castelo R, Roverato A (2006) A robust procedure for gaussian graphical model search from microarray data with p larger than n. J Mach Learn Res 6:2621–2650

    MathSciNet  MATH  Google Scholar 

  6. Castelo R, Siebes A (2000) Priors on network structures. Biasing the search for Bayesian networks. Int J Approx Reason 24:39–57. https://doi.org/10.1016/S0888-613X(99)00041-9

    Article  MathSciNet  MATH  Google Scholar 

  7. Dai J, Ren J, Du W (2020) Decomposition-based Bayesian network structure learning algorithm using local topology information. Knowl-Based Syst 195:1–14. https://doi.org/10.1016/j.knosys.2020.105602

    Article  Google Scholar 

  8. Di R, Gao X, Guo Z (2017) Bayesian networks structure learning based on improved BIC scoring. Syst Eng Electron 39:437–444. https://doi.org/10.3969/j.issn.1001-506X.2017.02.31

    Article  Google Scholar 

  9. Guo H, Li H (2020) An efficient bayesian network structure learning algorithm using the strategy of two-stage searches. Intell Data Anal 24:1087–1106. https://doi.org/10.3233/IDA-194844

    Article  Google Scholar 

  10. He C, Gao X, Gao K (2020) MMOS+ ordering search method for Bayesian network structure learning and its application. Chin J Electron 29:147–153. https://doi.org/10.1049/cje.2019.11.004

    Article  Google Scholar 

  11. Hui L, Fuli W, Hongru L (2019) Integrating expert knowledge for Bayesian network structure learning based on intuitionistic fuzzy set and Genetic Algorithm. Intell Data Anal 23:41–56. https://doi.org/10.3233/IDA-183877

    Article  Google Scholar 

  12. Komurlu C, Shao J, Akar B, Bayrak ES, Brey EM, Cinar A, Bilgic M (2016) Active inference for dynamic Bayesian networks with an application to tissue engineering. Knowl Inf Syst 50:917–943. https://doi.org/10.1007/s10115-016-0963-7

    Article  Google Scholar 

  13. Lam W, Bacchus F (2010) Learning bayesian belief networks: an approach based on the mdl principle. Comput Intell-Us 10:269–293. https://doi.org/10.1111/j.1467-8640.1994.tb00166.x

    Article  Google Scholar 

  14. Lee S, Kim S (2019) Identification of I-equivalent subnetworks in Bayesian networks to incorporate experts’ knowledge. Expert Syst 36:1–13. https://doi.org/10.1111/exsy.12346

    Article  Google Scholar 

  15. Lee S, Kim S (2020) Parallel simulated annealing with a greedy algorithm for Bayesian network structure learning. Ieee T Knowl Data En 32:1157–1166. https://doi.org/10.1109/TKDE.2019.2899096

    Article  Google Scholar 

  16. Li H, Guo H (2018) A hybrid structure learning algorithm for bayesian network using experts’ knowledge. Entropy-Switz 20:1–20. https://doi.org/10.3390/e20080620

    Article  MathSciNet  Google Scholar 

  17. Liu F, Zhang S, Guo W, Wei Z, Chen L (2016) Inference of gene regulatory network based on local Bayesian networks. Plos Comput Biol 12:1005–1024. https://doi.org/10.1371/journal.pcbi.1005024

    Article  Google Scholar 

  18. Liu H, Zhou S, Lam W, Guan J (2017) A new hybrid method for learning Bayesian networks: separation and reunion. Knowl-Based Syst 121:185–197. https://doi.org/10.1016/j.knosys.2017.01.029

    Article  Google Scholar 

  19. Chaudhary MS, Ranshous S, Samatova NF (2017) A community-driven graph partitioning method for constraint-based causal discovery[C]. In: International workshop on complex networks & their applications. Springer, Cham, 2017. In: International workshop on complex networks and their applications, pp 253–264

  20. Madsen A, Jensen F, Salmerón A, Langseth H, Nielsen T (2017) A parallel algorithm for Bayesian network structure learning from large data sets. Knowl-Based Syst 117:46–55

    Article  Google Scholar 

  21. Nägele A, Dejori M, Stetter M (2007) Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures. In: European Conference on Machine Learning, Warsaw, Poland, pp 238-249

  22. Qi X, Fan X, Gao Y, Liu Y (2019) Learning Bayesian network structures using weakest mutual-information-first strategy. Int J Approx Reason 114:84–98. https://doi.org/10.1016/j.ijar.2019.08.004

    Article  MathSciNet  MATH  Google Scholar 

  23. Wang C, Liu S, Zhu M (2012) Bayesian network learning algorithm based on unconstrained optimization and ant colony optimization. J Syst Eng Electron 23:784–790. https://doi.org/10.1109/JSEE.2012.00096

    Article  Google Scholar 

  24. Wille A, Bühlmann P (2006) Low-order conditional independence graphs for inferring genetic networks. Statal Appl Genet Mol Biol 5:1–32. https://doi.org/10.2202/1544-6115.1170

    Article  MathSciNet  MATH  Google Scholar 

  25. Xie X, Geng Z (2008) A recursive method for structural learning of directed acyclic graphs. J Mach Learn Res 9:459–483. https://doi.org/10.1145/1390681.1390695

    Article  MathSciNet  MATH  Google Scholar 

  26. Xie X, Geng Z, Zhao Q (2006) Decomposition of structural learning about directed acyclic graphs. Artif Intell 170:422–439. https://doi.org/10.1016/j.artint.2005.12.004

    Article  MathSciNet  MATH  Google Scholar 

  27. Xing Y, Chen S, Zhu S, Lu J (2020) Analysis factors that influence escalator-related injuries in metro stations based on bayesian networks: a case study in China. Int J Environ Res Public Health 17:1–21. https://doi.org/10.3390/ijerph17020481

    Article  Google Scholar 

  28. Xu J, Zhao Y, Chen J, Han C (2015) A structure learning algorithm for bayesian network using prior knowledge. J Comput Sci Tech-Ch 30:713–724. https://doi.org/10.1007/s11390-015-1556-8

    Article  MathSciNet  Google Scholar 

  29. Yet B, Perkins ZB, Tai NRM, Marsh DWR (2016) Clinical evidence framework for Bayesian networks. Knowl Inf Syst 50:117–143. https://doi.org/10.1007/s10115-016-0932-1

    Article  Google Scholar 

  30. Zhu M, Liu S (2012) A decomposition algorithm for learning bayesian networks based on scoring function. J Appl Math 10:1–17. https://doi.org/10.1155/2012/974063

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (61973067).

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  1. College of Information Science and Engineering, Northeastern University, No. 11 St. 3, Wenhua Road, Heping District, P.O. Box 135, Shenyang, 110819, Liaoning, People’s Republic of China

    Huiping Guo & Hongru Li

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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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