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Interpretation of hidden node methodology with network accuracy

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Title: Interpretation of hidden node methodology with network accuracy
Authors: Bang, J-W
Pappas, A
Gillies, D
Item Type: Report
Abstract: Bayesian networks are constructed under a con-ditional independency assumption. This assump-tion however does not necessarily hold in prac-tice and may lead to loss of accuracy. We previ-ously proposed a hidden node methodology whereby Bayesian networks are adapted by the addition of hidden nodes to model the data de-pendencies more accurately. Empirical results in a computer vision application to classify and count the neural cell automatically showed that a modified network with two hidden nodes achieved significantly better performance with an average prediction accuracy of 83.9% com-pared to 59.31% achieved by the original net-work. In this paper we justify the improvement of performance by examining the changes in network accuracy using four network accuracy measurements; the Euclidean accuracy, the Co-sine accuracy, the Jensen-Shannon accuracy and the MDL score. Our results consistently show that the network accuracy improves by introduc-ing hidden nodes. Consequently, we were able to verify that the hidden node methodology helps to improve network accuracy and contribute to the improvement of prediction accuracy.
Issue Date: 1-Jan-2003
URI: http://hdl.handle.net/10044/1/95629
DOI: https://doi.org/10.25561/95629
Publisher: Department of Computing, Imperial College London
Start Page: 1
End Page: 6
Journal / Book Title: Departmental Technical Report: 03/2
Copyright Statement: © 2003 The Author(s). This report is available open access under a CC-BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Publication Status: Published
Article Number: 03/2
Appears in Collections:Computing
Computing Technical Reports



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