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

Authors:
Kousuke Shibata;Hidehiro Nakano;Arata Miyauchi
Affiliations:
Department of Computer Science, Tokyo City University, Tokyo, Japan 158-8557;Department of Computer Science, Tokyo City University, Tokyo, Japan 158-8557;Department of Computer Science, Tokyo City University, Tokyo, Japan 158-8557
Venue:
Artificial Life and Robotics
Year:
2011

Citing 3
Cited 0

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Dynamic bayesian networks: representation, inference and learning
Evolving dynamic Bayesian networks with Multi-objective genetic algorithms

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Handling multiple objectives with particle swarm optimization

IEEE Transactions on Evolutionary Computation

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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.