Particle Swarm Optimization Learning of Bayesian Network Structure
Yan Liu · Journal of Xiamen University of Technology · 2014
A Bayesian networks learning was put forward based on information theory with particle swarm optimization algorithm.With the information entropy as the highest scoring function,the network structure complexity was constrained,and particle position and velocity vector operation designed,to solve the defects in using KL distance alone for search.In relatively large search space in the network structure,the optimization algorithm can obtain convergence in a short period of time to achieve fairly accurate network structure,and the algorithm and validation implemented through simulation experiment.The experimental results show that the algorithm has good effects in time and for precision.