LEARNING AND EVALUATING BAYESIAN NETWORK EQUIVALENCE CLASSES FROM INCOMPLETE DATA
Hanen Borchani, Nahla Ben Amor, Fédia Khalfallah · International Journal of Pattern Recognition and Artificial Intelligence · 2008
In this paper, we propose a new method, named Greedy Equivalence Search-Expectation Maximization (GES-EM), for learning Bayesian networks from incomplete data. Our method extends the recently proposed Greedy Equivalence Search (GES) algorithm10 to deal with incomplete data. For the quality evaluation of learned networks, we make use of the expected Bayesian Information Criterion (BIC) scoring function. In addition, we propose a new structural evaluation criterion. This so-called SEC criterion is more suitable than existing structural evaluation criteria, since it is based on the comparison of learned networks to the generating ones through Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show that GES-EM algorithm yields more accurate structures than the standard Alternating Model Selection-Expectation Maximization (AMS-EM) algorithm.15.