Induction of ripple-down rules applied to modeling large databases
Journal of Intelligent Information Systems
Refinement complements verification and validation
International Journal of Human-Computer Studies - Special issue: verification and validation
Inconsistency Tests for Patient Records in a Coronary Heart Disease Database
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Detecting the Knowledge Boundary with Prudence Analysis
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
ACM Computing Surveys (CSUR)
Two decades of ripple down rules research
The Knowledge Engineering Review
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online knowledge validation with prudence analysis in a document management application
Expert Systems with Applications: An International Journal
Experience with long-term knowledge acquisition
Proceedings of the sixth international conference on Knowledge capture
Detecting anomalies and intruders
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
RM and RDM, a preliminary evaluation of two prudent RDR techniques
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Review: Intrusion detection system: A comprehensive review
Journal of Network and Computer Applications
Situated cognition and knowledge acquisition research
International Journal of Human-Computer Studies
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As knowledge bases become more complex it is increasingly unlikely that they will have been validated against all possible data and therefore an increasing risk of making errors. Run-time validation is checking whether the output of a knowledge base for some data is likely to be correct at the time the data is processed. We have investigated various techniques for runtime validation. The most successful technique has been to constantly re-build a separate knowledge base using a different learning technique with cases labeled by the knowledge base being validated, as training data. Any new cases are processed by both knowledge bases and if the knowledge bases disagree the case is referred for manual checking as a possible outlier. If an outlier is detected the knowledge base is edited to give the correct answer and as cases are processed they are added to the training data for the machine learning knowledge base.