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International Conference on Intelligent Information Technologies for Industry

IITI 2019: Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19) pp 214–222Cite as

Algebraic Bayesian Networks: Parallel Algorithms for Maintaining Local Consistency

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1156))

Abstract

Algebraic Bayesian networks belong to the class of machine-learning probabilistic graphical models. One of the main tasks during researching machine learning models is the optimization of their time of work. This paper presents approaches to parallelizing algorithms for maintaining local consistency in algebraic Bayesian networks as one of the ways to optimize their time of work. An experiment provided to compare the time of parallel and nonparallel realizations of algorithms for maintaining local consistency.

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References

  1. Pelissari, R., Oliveira, M.C., Ben Amor, S., Abackerli, A.J.: A new FlowSort-based method to deal with information imperfections in sorting decision-making problems. Eur. J. Oper. Res. 276(10), 235–246 (2019). https://doi.org/10.1016/j.ejor.2019.01.006

    Article  MathSciNet  MATH  Google Scholar 

  2. Saha, I., Sarkar, J.P., Maulik, U.: Integrated rough fuzzy clustering for categorical data analysis. Fuzzy Sets Syst. 361, 1–32 (2019). https://doi.org/10.1016/j.fss.2018.02.007

    Article  MathSciNet  Google Scholar 

  3. Cheng, J., Wang, J.: An association-based evolutionary ensemble method of variable selection. Expert Syst. Appl. 124, 143–155 (2019). https://doi.org/10.1016/j.eswa.2019.01.039

    Article  Google Scholar 

  4. Tulupyev, A.L., Nikolenko, S.I., Sirotkin, A.V.: Fundamentals of the Theory of Bayesian Networks: Textbook. St.-Petersburg University, Saint-Petersburg (2019). (in Russian)

    Google Scholar 

  5. Ye, J., Li, J., Newman, M.G., Adams, R.B., Wang, J.Z.: Probabilistic multigraph modeling for improving the quality of crowdsourced affective data. IEEE Trans. Affect. Comput. 10(1), 115–128 (2019). https://doi.org/10.1109/TAFFC.2017.2678472

    Article  Google Scholar 

  6. Qiang, Y.-T., Fu, Y.-W., Yu, X., Guo, Y.-W., Zhou, Z.-H., Sigal, L.: Learning to generate posters of scientific papers by probabilistic graphical models. J. Comput. Sci. Technol. 34(1), 155–169 (2019). https://doi.org/10.1007/s11390-019-1904-1

    Article  Google Scholar 

  7. Vogel, K., Weise, L., Schroter, K., Thieken, A.H.: Identifying driving factors in flood-damaging processes using graphical models. Water Resour. Res. 54(11), 8864–8889 (2018). https://doi.org/10.1029/2018WR022858

    Article  Google Scholar 

  8. Buscombe, D., Grams, P.E.: Probabilistic substrate classification with multispectral acoustic backscatter: a comparison of discriminative and generative models. Geosciences 8(11) (2018). Article no. UNSP395. https://doi.org/10.3390/geosciences8110395

  9. Huang, Z.M., Yang, L., Jiang, W.: Uncertainty measurement with belief entropy on the interference effect in the quantum-like Bayesian Network. Appl. Math. Comput. 347, 417–428 (2019). https://doi.org/10.1016/j.amc.2018.11.036

    Article  MathSciNet  MATH  Google Scholar 

  10. Marella, D., Vicard, P.: Toward an integrated Bayesian network approach to measurement error detection and correction. Commun. Stat.-Simul. Comput. 48(2), 544–555 (2019). https://doi.org/10.1080/03610918.2017.1387664

    Article  MathSciNet  Google Scholar 

  11. Suwanwimolkul, S., Zhang, L., Gong, D., Zhang, Z., Chen, C., Ranasinghe, D.C., Shi, J.Q.: An adaptive Markov random field for structured compressive sensing. IEEE Trans. Image Process. 28(3), 1556–1570 (2019). https://doi.org/10.1109/TIP.2018.2878294

    Article  MathSciNet  Google Scholar 

  12. Dolgiy, A.I., Kovalev, S.M., Kolodenkova, A.E.: Processing heterogeneous diagnostic information on the basis of a hybrid neural model of Dempster-Shafer. Commun. Comput. Inf. Sci. 934, 79–90 (2018). https://doi.org/10.1007/978-3-030-00617-4_8

    Article  Google Scholar 

  13. Ojha, V.K., Abraham, A., Snášel, V.: Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng. Appl. Artif. Intell. 60, 97–116 (2017). https://doi.org/10.1016/j.engappai.2017.01.013

    Article  Google Scholar 

  14. Tai, W.P., Teng, Q.Y., Zhou, Y.M., Zhou, J.P., Wang, Z.: Chaos synchronization of stochastic reaction-diffusion time-delay neural networks via non-fragile output-feedback control. Appl. Math. Comput. 354, 115–127 (2019). https://doi.org/10.1016/j.amc.2019.02.028

    Article  MathSciNet  MATH  Google Scholar 

  15. Tulupyev, A.L.: Algebraic Bayesian Networks: Global Logical and Probabilistic Inference in Joint Trees: A Tutorial, 2nd edn. SPb: VVM, Saint-Petersburg (2019). (in Russian)

    Google Scholar 

  16. Tulupyev, A.L.: Algebraic Bayesian Networks: Local Logical and Probabilistic Inference: A Tutorial, 2nd edn. SPb: VVM, Saint-Petersburg (2019). (in Russian)

    Google Scholar 

  17. Kharitonov, N.A., Maximov, A.G., Tulupyev, A.L.: Algebraic Bayesian networks: the use of parallel computing while maintaining various degrees of consistency. Studies in Systems, Decision and Control, vol. 199, pp. 696–704 (2019). https://doi.org/10.1007/978-3-030-12072-6_56

  18. Zhao, L., Zhou, Y.H., Lu, H.P., Fujita, H.: Parallel computing method of deep belief networks and its application to traffic flow prediction. Knowl.-Based Syst. 163, 972–987 (2019). https://doi.org/10.1016/j.knosys.2018.10.025

    Article  Google Scholar 

  19. Kharitonov N., Tulupyev A., Zolotin A.: Software implementation of reconciliation algorithms in algebraic Bayesian networks. In: Proceedings of 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 8–10 (2017). https://doi.org/10.1109/SCM.2017.7970479

  20. Mal’chevskaya, E.A., Berezin, A.I., Zolotin, A.A., Tulupyev, A.L.: Algebraic Bayesian networks: local probabilistic-logic inference machine architecture and set of minimal joint graphs. Advances in Intelligent Systems and Computing, vol. 451, pp. 69–79 (2016)

    Google Scholar 

  21. Abramov, M.V., Azarov, A.A.: Identifying user’s of social networks psychological features on the basis of their musical preferences. In: Proceedings of 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 90–92 (2017). https://doi.org/10.1109/SCM.2017.7970504

  22. Bagretsov, G.I., Shindarev, N.A., Abramov, M.V., Tulupyeva, T.V.: Approaches to development of models for text analysis of information in social network profiles in order to evaluate user’s vulnerabilities profile. In: Proceedings of 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 93–95 (2017). https://doi.org/10.1109/SCM.2017.7970505

  23. Shindarev, N., Bagretsov, G., Abramov, M., Tulupyeva, T., Suvorova, A.: Approach to identifying of employees profiles in websites of social networks aimed to analyze social engineering vulnerabilities. Advances in Intelligent Systems and Computing, vol. 679, pp. 441–447 (2018)

    Google Scholar 

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Acknowledgments

The research was carried out in the framework of the project on SPIIRAS governmental assignment No. 0073-2019-0003, with the financial support of the RFBR (project No. 18-01-00626: Methods of representation, synthesis of truth estimates and machine learning in algebraic Bayesian networks and related knowledge models with uncertainty: the logic-probability approach and graph systems).

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Authors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14-th Line Vasilyevsky Ostrov, St. Petersburg, 199178, Russia

    Nikita A. Kharitonov, Anatolii G. Maksimov & Alexander L. Tulupyev

  2. St. Petersburg State University, 7-9, University Embankment, St. Petersburg, 199034, Russia

    Anatolii G. Maksimov & Alexander L. Tulupyev

Authors
  1. Nikita A. Kharitonov
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  2. Anatolii G. Maksimov
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  3. Alexander L. Tulupyev
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Corresponding author

Correspondence to Anatolii G. Maksimov .

Editor information

Editors and Affiliations

  1. Rostovskogo Strelkovogo Polka Narodnogo, Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  2. Bauman Moscow State Technical University, Moscow, Russia

    Valery Tarassov

  3. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Vaclav Snasel

  4. Rostovskogo Strelkovogo Polka, Rostov State Transport University, Rostov-on-Don, Russia

    Andrey Sukhanov

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Kharitonov, N.A., Maksimov, A.G., Tulupyev, A.L. (2020). Algebraic Bayesian Networks: Parallel Algorithms for Maintaining Local Consistency. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_22

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