Skip to Main Content
Learning the structure of a Bayesian network (BN)from a data set is NP-hard. In this paper, we discuss a novel heuristic based on estimation of distribution algorithms (EDA), a new paradigm for evolutionary computation that is used as a search engine in the BN structure learning problem. The purpose of this work is to study the parameter setting of the EDA and to fix a "good" set of parameters. For this purpose, the EDA-based procedure is applied on several benchmarks to recover the original structure from data. The quality of the learned structure is assessed using several performance indexes.
Digital Information Management, 2007. ICDIM '07. 2nd International Conference on (Volume:1 )
Date of Conference: 28-31 Oct. 2007