Enhancing Automated Test Selection in Probabilistic Networks

D. Sent, L.C. van der Gaag

    Research output: Book/ReportReportProfessional

    2 Citations (Scopus)
    65 Downloads (Pure)

    Abstract

    In diagnostic decision-support systems, test selection amounts to selecting, in a sequential manner, a test that is expected to yield the largest decrease in the uncertainty about a patient’s diagnosis. For capturing this uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that the Gini index can be regarded as an approximation of the Shannon entropy and that the misclassification error can be looked upon as an approximation of the Gini index. We further argue that the differences between the first derivatives of the three functions can explain different test sequences in practice. Experimental results from using the measures with a real-life probabilistic network in oncology support our observations.
    Original languageUndefined
    Place of PublicationEnschede
    PublisherCentre for Telematics and Information Technology (CTIT)
    Number of pages10
    Publication statusPublished - Feb 2007

    Publication series

    NameCTIT Technical Report Series
    PublisherCentre for Telematics and Information Technology, University of Twente
    No.2/TR-CTIT-07-11
    ISSN (Print)1381-3625

    Keywords

    • EWI-9248
    • METIS-242038
    • IR-66936

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