Bayesian network structure learning algorithm based on mutual information
Yihu Chen · Computer Engineering and Applications Journal · 2012
Structure learning is one of the most important branches in Bayesian network, while learning Bayesian network structures from data is NP-complete. An improved algorithm is provided for learning Bayesian network structures from data. It constructs the initial undirected graph based on mutual information, and then orients undirected edges by using conditional independence tests, additionally a local optimal method for four-node and five-node loops is proposed to construct the initial draft about the structure, finally greedy search is performed to explore the optimal structure. Numerical experiments show that both the BIC score and structure error have some improvements, and the number of iterations and running time are greatly reduced. Therefore the structure with highest degree of data matching can be relatively faster determined by the improved algorithm.