Improved Bayesian Networks Structure Learning Algorithm
Xiaodong Xian · Jisuanji fangzhen · 2008
Bayesian networks structure learning is one of main research techniques in the field of data mining and knowledge discovering,which can find underlying probabilistic dependence relationships between variables and knowledge expression model from a great deal of data,and support modeling and resolving for complex decision-making tasks,so that it has an import research signification. According to analyzing classical Structure Learning methods (K2 and MCMC algorithms),an improved Bayesian networks Structure Learning algorithm was proposed combined with the merits of above two algorithms and the idea of model averaging. Experiment results show that the proposed algorithm can cover shortages of K2 and MCMC algorithms and can quickly achieve a comparative correct and steady model structure without priori knowledge.