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A learning method for dynamic Bayesian network structures using a multi-objective particle swarm optimizer

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Abstract

In this article, we present a multi-objective discrete particle swarm optimizer (DPSO) for learning dynamic Bayesian network (DBN) structures. The proposed method introduces a hierarchical structure consisting of DPSOs and a multi-objective genetic algorithm (MOGA). Groups of DPSOs find effective DBN sub-network structures and a group of MOGAs find the whole of the DBN network structure. Through numerical simulations, the proposed method can find more effective DBN structures, and can obtain them faster than the conventional method.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Tokyo City University, 1-28-1 Tamazutsumi, Setagaya-ku, Tokyo, 158-8557, Japan

    Kousuke Shibata, Hidehiro Nakano & Arata Miyauchi

Authors
  1. Kousuke Shibata
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  2. Hidehiro Nakano
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  3. Arata Miyauchi
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Corresponding author

Correspondence to Hidehiro Nakano.

Additional information

This work was presented in part at the 16th International Symposium on Artificial Life and Robotics, Oita, Japan, January 27–29, 2011

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Shibata, K., Nakano, H. & Miyauchi, A. A learning method for dynamic Bayesian network structures using a multi-objective particle swarm optimizer. Artif Life Robotics 16, 329–332 (2011). https://doi.org/10.1007/s10015-011-0943-7

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  • DOI: https://doi.org/10.1007/s10015-011-0943-7

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