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Unsupervised Evolutionary Algorithm for Dynamic Bayesian Network Structure Learning

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Advanced Methodologies for Bayesian Networks (AMBN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9505))

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Abstract

The introduction of temporal dimension makes it difficult and complex to learn dynamic Bayesian network (DBN) structure for huge search space, hence many studies focus on some particular types of DBN, such as dynamic Naive Bayesian Classifier (DNBC). In order to overcome the limited applicability of DBN structure learning methods, this paper proposes an unsupervised evolutionary algorithm in which the selection of initial population has been implemented by means of mutual information to reduce the search space. Furthermore, in view of the poor performance of traditional encoding scheme and the recount of Bayesian information criterion (BIC) score when calculating the individual fitness, we provide a new structure representation without a necessity of the acyclicity test and an updating algorithm for BIC scores with the help of family inheritance to improve the efficiency. Simulations on synthetic data demonstrate that the proposed unsupervised evolutionary algorithm is effective in DBN structure learning.

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Acknowledgments

This paper was supported by the International S&T Cooperation Projects of China (2015DFR10510), the National Natural Science Foundation of China (61162010; 61440048; 61562018), and the Natural Science Foundation of Hainan Province, China (614227).

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Authors and Affiliations

  1. College of Information Science and Technology, Hainan University, Haikou, 570228, China

    Jingguo Dai & Jia Ren

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  1. Jingguo Dai
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  2. Jia Ren
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Correspondence to Jia Ren .

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Editors and Affiliations

  1. Osaka University, Osaka, Japan

    Joe Suzuki

  2. The University of Electro-Communications, Tokyo, Japan

    Maomi Ueno

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Dai, J., Ren, J. (2015). Unsupervised Evolutionary Algorithm for Dynamic Bayesian Network Structure Learning. In: Suzuki, J., Ueno, M. (eds) Advanced Methodologies for Bayesian Networks. AMBN 2015. Lecture Notes in Computer Science(), vol 9505. Springer, Cham. https://doi.org/10.1007/978-3-319-28379-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-28379-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28378-4

  • Online ISBN: 978-3-319-28379-1

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