An Approach to Learning Bayesian Network by Using Infeasible Solutions

Abstract:

Existing Bayesian network learning approaches based on search & scoring usually work with feasible solutions which satisfy directed acyclic graph. This kind of approaches often removes infeasible solutions or converts infeasible solutions to feasible solutions when the solutions are infeasible. However, some infeasible solutions maybe have more useful information. This paper proposes the ISEC method for learning Bayesian network by using feasible and infeasible solutions synchronously based on an infeasible solution selection strategy. Then, the method can take advantage of the information in the infeasible solutions. Experiments show that the proposed approach can achieve better performance in less time than the approaches which only use feasible solutions.

Key words:machine learning; Bayesian network; structure learning; minimum description length; evolutionary computing