Learning dynamic Bayesian network with immune evolutionary algorithm

Haiyang Jia; Dayou Liu; Peng Yu

doi:10.1109/icmlc.2005.1527444

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Learning dynamic Bayesian network with immune evolutionary algorithm

Haiyang Jia Dayou Liu Peng Yu

Year: 2005 Pages: 2934-2938 Vol. 5

DOI: 10.1109/icmlc.2005.1527444

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Abstract

Dynamic Bayesian networks (DBNs) are directed graphical models of stochastic processes. How to learn the structure of DBNs from data is a hot problem of research. In this paper, the author presents an immune evolutionary algorithm for learning the network structure of DBNs. The results of contrast experiment prove that the constringency rate is more rapid than EGA-DBN algorithms.

Keywords:

Dynamic Bayesian network Graphical model Bayesian network Computer science Artificial intelligence Contrast (vision) Bayesian probability Evolutionary algorithm Machine learning Algorithm

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Bayesian Modeling and Causal Inference

Physical Sciences → Computer Science → Artificial Intelligence

Artificial Immune Systems Applications

Physical Sciences → Engineering → Biomedical Engineering

Learning dynamic Bayesian network with immune evolutionary algorithm

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