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Learning the structure of a Bayesian Network (BN) with score-based solutionsinvolves exploring the search space of possible graphs and moving towards thegraph that maximises a given objective function. Some algorithms offer exactsolutions that guarantee to return the graph with the highest objective score,while others offer approximate solutions in exchange for reduced computationalcomplexity. This paper describes an approximate BN structure learningalgorithm, which we call Model Averaging Hill-Climbing (MAHC), that combinestwo novel strategies with hill-climbing search. The algorithm starts by pruningthe search space of graphs, where the pruning strategy can be viewed as anaggressive version of the pruning strategies that are typically applied tocombinatorial optimisation structure learning problems. It then performs modelaveraging in the hill-climbing search process and moves to the neighbouringgraph that maximises the objective function, on average, for that neighbouringgraph and over all its valid neighbouring graphs. Comparisons with otheralgorithms spanning different classes of learning suggest that the combinationof aggressive pruning with model averaging is both effective and efficient,particularly in the presence of data noise.

Effective and efficient structure learning with pruning and model averaging strategies