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Explaining Probabilistic Fault Diagnosis and Classification using Case-based Reasoning
RISE, Swedish ICT, SICS. RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
RISE, Swedish ICT, SICS. RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
Number of Authors: 4
2014 (English)Conference paper, (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2014, 6.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-24345OAI: oai:DiVA.org:ri-24345DiVA: diva2:1043426
Conference
22nd International Conference on Case-Based Reasoning
Available from: 2016-10-31 Created: 2016-10-31Bibliographically approved

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