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The determination of Bayesian network structure, especially in the case of large domains, can be complex, time consuming and imprecise. Therefore, in the last years, the interest of the scientific community in learning Bayesian network structure from data is increasing. This interest is motivated by the fact that many techniques or disciplines, as data mining, text categorization, ontology building, can take advantage from structural learning. In literature we can find many structural learning algorithms but none of them provides good results in every case or dataset. In this paper we introduce a method for structural learning of Bayesian networks based on a multiexpert approach. Our method combines the outputs of five structural learning algorithms according to a majority vote combining rule. The combined approach shows a performance that is better than any single algorithm. We present an experimental validation of our algorithm on a set of “de facto” standard networks, measuring performance both in terms of the network topological reconstruction and of the correct orientation of the obtained arcs.

A BAYESIAN NETWORKS STRUCTURAL LEARNING ALGORITHM BASED ON A MULTIEXPERT APPROACH