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An empirical evaluation of costs and benefits of simplifying Bayesian networks by removing weak arcs

Ratnapinda, P and Druzdzel, MJ (2014) An empirical evaluation of costs and benefits of simplifying Bayesian networks by removing weak arcs. In: UNSPECIFIED.

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Abstract

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We report the results of an empirical evaluation of structural simplification of Bayesian networks by removing weak arcs. We conduct a series of experiments on six networks built from real data sets selected from the UC Irvine Machine Learning Repository. We systematically remove arcs from the weakest to the strongest, relying on four measures of arc strength, and measure the classification accuracy of the resulting simplified models. Our results show that removing up to roughly 20 percent of the weakest arcs in a network has minimal effect on its classification accuracy. At the same time, structural simplification of networks leads to significant reduction of both the amount of memory taken by the clique tree and the amount of computation needed to perform inference.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Ratnapinda, P
Druzdzel, MJdruzdzel@pitt.eduDRUZDZEL
Date: 1 January 2014
Date Type: Publication
Journal or Publication Title: Proceedings of the 27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014
Page Range: 508 - 511
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 02 Jul 2014 17:09
Last Modified: 01 Nov 2017 12:58
URI: http://d-scholarship.pitt.edu/id/eprint/22167

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