Description

This paper presents an efficient algorithm for learning a Bayesian belief network (BBN) structure from a database, as well as providing a comparison between two BBN structure fitness functions. A Bayesian belief network is a directed acyclic graph representing conditional expectations. In this paper, we propose a two-phase algorithm. The first phase uses asymptotically correct structure learning for efficient search space exploration, while the second phase uses greedy model selection for accurate search space exploration. The minimum description length (MDL) structure fitness function is also compared with the database given model probability (DGM) fitness function in the second phase. The model selection algorithms are applied to the ALARM network to provide a comparison for the accuracy of the techniques.

Date of creation, presentation, or exhibit

10-5-2003

Comments

IEEE International Conference on Systems, Man and Cybernetics 5 (2003) 4601-4606 "A two phase approach to Bayesian network model selection and comparison between the MDL and DGM scoring heuristics," IEEE International Conference on Systems, Man and Cybernetics. Institute of Electrical and Electronics Engineers. Held in Washington, D.C.: 5-8 October 2003. ©2003 Institute of Electrical and Electronics Engineers (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. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ISBN: 0-7803-7952-7 ISSN: 1062-922X Note: 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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