Title :
Learning dynamic Bayesian network with immune evolutionary algorithm
Author :
Jia, Hai-yang ; Liu, Da-you ; Yu, Peng
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
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 :
Bayes methods; belief networks; evolutionary computation; learning (artificial intelligence); stochastic processes; constringency rate; directed graphical model; dynamic Bayesian network; immune evolutionary algorithm; stochastic process; Artificial immune systems; Bayesian methods; Convergence; Evolutionary computation; Graphical models; Immune system; Machine learning; Machine learning algorithms; Probability distribution; Stochastic processes; Dynamic Bayesian Network; Immune evolutionary algorithm; Structural Learning;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527444
