Bayesian Network Structure Learning Based on Improved Particle Swarm Optimization
JIANG Hao
JIANG Bing
Abstract：Bayesian network structure learning is one of the important research techniques in the do‐main of data mining and know ledge discovery ,w hen the search space of the netw ork structure is bigger , traditional binary particle algorithms often have some defects such as low convergent speed ,falling easily into local optimum and low precision We improve the classic binary particle swarm optimization algo‐rithm in two respects:particle initialization and update process ;the improved algorithm has stronger op‐timization ability .We compare the proposed algorithm with the original algorithm using the ASIA net‐work .The results and their analysis show preliminarily that the proposed algorithm is able to find the better solution with less number of iterations ,without increasing the complexity basically .
Keywords：Bayesian networksdata miningparticles warm optimization (PSO)
Publication Date：2015-01-01
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：5( 83-87 )