Improved algorithm based on mutual information for learning Bayesian network structures in the space of equivalence classes

As is well known, greedy algorithm is usually used as local optimization method in many heuristic algorithms such as ant colony optimization, taboo search, and genetic algorithms, and it is significant to increase the convergence speed and learning accuracy of greedy search in the space of equivalence classes of Bayesian network structures. An improved algorithm, I-GREEDY-E is presented based on mutual information and conditional independence tests to firstly make a draft about the real network, and then greedily explore the optimal structure in the space of equivalence classes starting from the draft. Numerical experiments show that both the BIC score and structure error have some improvement, 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.