Learning Bayesian Network Structure Based on Ant Colony Optimization Algorithms

To learn Bayesian Network (BN) structure from incomplete data,this paper proposed an approach combined with both processes of data completing and Ant Colony Optimization (ACO).First,unobserved data are randomly initialized,thus a complete data is got.Based on such a data set,an initialization BN is learned by Ant Colony Algorithm.Second,in light of the current best structure of evolutionary process,Expectation Maximization (EM)estimating and randomly sampling are performed to complete the data.Third,on the basis of the new complete data set,the BN structure is evolved by an improved ACO process.Finally,the second and third steps are iterated until the global best structure is obtained.Experimental results show the approach can effectively learn BN structure form incomplete data,and is more accurate than MS-EM、EGA、BN-GS algorithms.