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Case-Based Reasoning for Explaining Probabilistic Machine Learning
RISE., Swedish ICT, SICS.ORCID-id: 0000-0002-9890-4918
RISE., Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID-id: 0000-0001-8952-3542
2014 (engelsk)Inngår i: International Journal of Computer Science and Information Technology, Vol. 6, s. 87-101Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As 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. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

sted, utgiver, år, opplag, sider
2014, 7. Vol. 6, s. 87-101
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-24342DOI: 10.5121/ijcsit.2014.6206OAI: oai:DiVA.org:ri-24342DiVA, id: diva2:1043422
Tilgjengelig fra: 2016-10-31 Laget: 2016-10-31 Sist oppdatert: 2020-12-01bibliografisk kontrollert

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Gillblad, Daniel

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