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Proceedings Paper

Learning Bayesian network structure using a cloud-based adaptive immune genetic algorithm
Author(s): Qin Song; Feng Lin; Wei Sun; KC Chang
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Paper Abstract

A new BN structure learning method using a cloud-based adaptive immune genetic algorithm (CAIGA) is proposed. Since the probabilities of crossover and mutation in CAIGA are adaptively varied depending on X-conditional cloud generator, it could improve the diversity of the structure population and avoid local optimum. This is due to the stochastic nature and stable tendency of the cloud model. Moreover, offspring structure population is simplified by using immune theory to reduce its computational complexity. The experiment results reveal that this method can be effectively used for BN structure learning.

Paper Details

Date Published: 5 May 2011
PDF: 10 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500S (5 May 2011); doi: 10.1117/12.883013
Show Author Affiliations
Qin Song, Zhejiang Univ. (China)
Feng Lin, Zhejiang Univ. (China)
George Mason Univ. (United States)
Wei Sun, George Mason Univ. (United States)
KC Chang, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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