Quality and Reliability Engineering International

Suggesting and Justifying Model Updates for Improved Troubleshooting

Journal Article

For the troubleshooting domain, machine learning is used to suggest model updates using in‐service troubleshooting and component testing records. One of the challenges of using these updates is how to justify each change to the system experts; a novel approach for the justification of updates to a Bayesian network troubleshooting model is presented. The results of experiments into the performance of the approach suggest that the changes suggested by maximum likelihood learning improve a model by fitting to the new records and a good justification for each change can be obtained from quantities already computed during the learning process. Copyright © 2012 John Wiley & Sons, Ltd.

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