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Abstract

The present invention includes a mechanism for applying expert knowledge and machine-learning routines to a continuous stream of information. The present method comprises learning a set of dependability models, one for each classification model, that characterize the situations in which each of the classification models is able to make correct predictions. At appropriate intervals the method produces new fault localization knowledge in the form of decision-tree based classification and dependability models. Such knowledge is used to enhance the existing classification knowledge already available. Each of these classification models has a particular sub-domain where it is the most reliable, and hence the best choice to use. For future unlabeled examples, these dependability models are consulted to select the most appropriate classification model, and the prediction of that classification model is then accepted.

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First Claim