Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings

An important type of methods for learning belief networks from data are those based on the use of a scoring metric, to evaluate the fitness of any given candidate network to the data base, and a search procedure to explore the set of candidate networks. In this paper we propose a new method that carries out the search not in the space of directed acyclic graphs but in the space of the orderings of the variables that compose the graphs. Moreover, we use a new stochastic search method to be applied to this problem, Variable Neighborhood Search. We also experimentally compare our methods with some other search procedures commonly used in the literature.

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