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

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.

[1]  Pedro Larrañaga,et al.  Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  David Maxwell Chickering,et al.  Learning Bayesian Networks is NP-Complete , 2016, AISTATS.

[3]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

[4]  Wang Xiufeng,et al.  Learning Bayesian networks using genetic algorithm , 2007 .

[5]  Dai Chao-hua.  Adaptive genetic algorithm based on cloud theory , 2007 .

[6]  Feng Lin,et al.  The convergence analysis of parallel genetic algorithm based on allied strategy , 2010, Defense + Commercial Sensing.

[7]  Sun Jian-ying.  Bayesian network based on information theory and immune genetic algorithm , 2006 .

[8]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[9]  T. J. Jia.  Construction of Learning Algorithm based on SGA Bayesian Network , 2008, 2008 International Symposium on Electronic Commerce and Security.

[10]  David Heckerman,et al.  Bayesian Networks for Data Mining , 2004, Data Mining and Knowledge Discovery.

[11]  In-Cheol Kim,et al.  Using Bayesian Networks to Analyze Medical Data , 2003, MLDM.

[12]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[13]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[14]  Ju-Jang Lee,et al.  Bayesian network-based non-parametric compact genetic algorithm , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[15]  Lin Jianhui.  Cloud Model Based Genetic Algorithm and Its Applications , 2007 .

[16]  David Maxwell Chickering,et al.  Large-Sample Learning of Bayesian Networks is NP-Hard , 2002, J. Mach. Learn. Res..