Learning Bayesian network structure using a cloud-based adaptive immune genetic algorithm

Qin Song; Feng Lin; Wei Sun; KC Chang

doi:10.1117/12.883013

5 May 2011 Learning Bayesian network structure using a cloud-based adaptive immune genetic algorithm

Qin Song, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 80500S (2011) https://doi.org/10.1117/12.883013
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Qin Song, Feng Lin, Wei Sun, and KC Chang "Learning Bayesian network structure using a cloud-based adaptive immune genetic algorithm", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500S (5 May 2011); https://doi.org/10.1117/12.883013

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