Learning Bayesian network structures by estimation of distribution algorithms : An experimental analysis
Résumé
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.
Origine : Fichiers produits par l'(les) auteur(s)
Loading...