REPRESENTATIVENESS AND UNCERTAINTY IN CLASSIFICATION SYSTEMS.

Paul Cohen, Alvah Davis, David Day, Michael Greenberg, Rick Kjeldsen, Susan Lander, Cynthia Loiselle

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

The choice of implication as a representation for empirical associations and for deduction as a mode of inference requires a mechanism extraneous to deduction to manage uncertainty associated with inference. Consequently, the interpretation of representations of uncertainty is unclear. Representativeness, of degree of fit, is proposed as an interpretation of degree of belief for classification tasks. The calculation of representativeness depends on the nature of the associations between evidence and conclusions. Patterns of associations are characterized as endorsements of conclusions. We discuss an expert system that uses endorsements to control the search for the most representative conclusion, given evidence. Some approaches from the biomedical field are examined.

Original languageEnglish (US)
Pages (from-to)136-149
Number of pages14
JournalAI Magazine
Volume6
Issue number3
StatePublished - Sep 1 1985

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Cohen, P., Davis, A., Day, D., Greenberg, M., Kjeldsen, R., Lander, S., & Loiselle, C. (1985). REPRESENTATIVENESS AND UNCERTAINTY IN CLASSIFICATION SYSTEMS. AI Magazine, 6(3), 136-149.