On the Behaviour of Information Measures for Test Selection

D. Sent, L.C. van der Gaag

    Research output: Book/ReportReportProfessional

    2 Citations (Scopus)
    118 Downloads (Pure)


    Most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, variables are selected sequentially, on a one-by-one basis, based upon expected information gain. While myopic test selection is not realistic for many medical applications, non-myopic test selection, in which information gain would be computed for all combinations of variables, would be too demanding. We present three new test-selection algorithms for probabilistic networks, which all employ knowledge-based clusterings of variables; these are a myopic algorithm, a non-myopic algorithm and a semi-myopic algorithm. In a preliminary evaluation, the semi-myopic algorithm proved to generate a satisfactory test strategy, with little computational burden.
    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
    ISSN (Print)1381-3625


    • METIS-242039
    • IR-66937
    • EWI-9250

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