Learning Bayesian Networks from Incomplete Databases
This addresses the limitation of existing methods that require complete data or use expensive iterative approaches, benefiting researchers and practitioners in machine learning and data analysis.
The paper tackles the problem of learning Bayesian network structures from incomplete databases, introducing a deterministic method that shows significant robustness and execution time independent of the number of missing data.
Bayesian approaches to learn the graphical structure of Bayesian Belief Networks (BBNs) from databases share the assumption that the database is complete, that is, no entry is reported as unknown. Attempts to relax this assumption involve the use of expensive iterative methods to discriminate among different structures. This paper introduces a deterministic method to learn the graphical structure of a BBN from a possibly incomplete database. Experimental evaluations show a significant robustness of this method and a remarkable independence of its execution time from the number of missing data.