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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Explaining Probabilistic Fault Diagnosis and Classification Using Case-Based Reasoning
verfasst von : Tomas Olsson, Daniel Gillblad, Peter Funk, Ning Xiong
Erschienen in: Case-Based Reasoning Research and Development
Verlag: Springer International Publishing
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This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of
the principle of interchangeability
that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.