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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 (English)In: International Journal of Computer Science and Information Technology, Vol. 6, p. 87-101Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2014, 7. Vol. 6, p. 87-101
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-24342OAI: oai:DiVA.org:ri-24342DiVA: diva2:1043422
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf