Learning Bayesian Network Structure Using Genetic Algorithm with Consideration of the Node Ordering via Principal Component Analysis
Vahid Rezaei Tabar, Maryam Mahdavi, Saghar Heidari, Sima Naghizadeh · Journal of the Iranian Statistical Society · 2016
The most challenging task in dealing with Bayesian networks is learning their structure.Two classical approaches are often used for learning Bayesian network structure: Constraint-Based method and Score-and-Search-Based one.However, neither the first nor the second one are completely satisfactory.Therefore, the heuristic search such as Genetic Algorithms with a fitness score function is considered for learning Bayesian network structure.To assure the closeness of the genetic operators, the ordering among variables (nodes) must be determined.In this paper, we determine the node ordering by considering the Principal Component Analysis (PCA).For this purpose, we first determine the appropriate correlation between variables and then use the absolute value of variable's coefficients in the first component.It means that a node X i can only