REPRESENTATIVENESS AND UNCERTAINTY IN CLASSIFICATION SYSTEMS.

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

    Research output: Contribution to journalArticlepeer-review

    21 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 1985

    ASJC Scopus subject areas

    • Artificial Intelligence

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