Fault Localization in Bayesian Networks
نویسندگان
چکیده
This paper considers the accuracy of classification using Bayesian networks (BNs). It presents a method to localize network parts that are (i) in a given (rare) case responsible for a potential misclassification, or (ii) modeling errors that consistently cause misclassifications, even in common cases. We analyze how inaccuracies introduced by such network parts are propagated through a network and derive a method to localize the source of the inaccuracy. The method is based on monitoring the BN's 'behavior' at runtime, specifically the correlation among a set of observations. Finally, when bad network parts are found, they can be repaired or their effects mitigated.
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