Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: a comparative study

This paper presents a comparative study of Bayesian belief network structure learning algorithms with a view to identify a suitable algorithm for modeling the contextual relations among objects typically found in natural imagery. Four popular structure learning algorithms are compared: two constraint-based algorithms (PC proposed by Spirtes and Glymour and Fast Incremental Association Markov Blanket proposed by Yaramakala and Margaritis), a score-based algorithm (Hill Climbing as implemented by Daly), and a hybrid algorithm (Max-Min Hill Climbing proposed by Tsamardinos et al.). Contrary to the belief regarding the superiority of constraint-based approaches, our empirical results show that a score-based approach performs better on our context dataset in terms of prediction power and learning time. The hybrid algorithm could achieve similar prediction performance as the score-based approach, but requires longer time to learn the desired network. Another interesting fact the study has revealed is the existence of strong correspondence between the linear correlation pattern within the dataset and the edges found in the learned networks.