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Explaining Probabilistic Fault Diagnosis and Classification using Case-based Reasoning

Fulltext:


Authors:

Tomas Olsson, Daniel Gillblad, Peter Funk, Ning Xiong

Research groups:

Artificial Intelligence and Intelligent Systems
Learning and Optimisation

Publication Type:

Conference/Workshop Paper

Venue:

22nd International Conference on Case-Based Reasoning

DOI:

10.1007/978-3-319-11209-1_26

Abstract

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.

Bibtex

@inproceedings{Olsson3663,
author = {Tomas Olsson and Daniel Gillblad and Peter Funk and Ning Xiong},
title = {Explaining Probabilistic Fault Diagnosis and Classification using Case-based Reasoning},
month = {September},
year = {2014},
booktitle = {22nd International Conference on Case-Based Reasoning},
url = {http://www.es.mdu.se/publications/3663-}
}