Description

Bayesian networks are directed acyclic graphs that model the dependency relationships between variables of interest. These networks are characterized by the structure of the network and the conditional probabilities that specify the dependencies that exist between the variables. In this paper, the scatter search optimization algorithm is utilized in learning the structure of the Bayesian network from complete data. This involves a heuristic search for the best network structure that maximizes a scoring function given a database of cases. The scatter search algorithm is implemented on a database of cases sampled from known networks in order to test the accuracy of the structural learning. Empirical results from the implementations are presented in the paper.

Date of creation, presentation, or exhibit

2004

Comments

Copyright 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ISBN: 0-7803-8566-7Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Microelectronic Engineering (KGCOE)

Campus

RIT – Main Campus

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