Enhancing Automated Test Selection in Probabilistic Networks

D. Sent, L.C. van der Gaag

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    Most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, test 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 study, the semi-myopic algorithm proved to generate a satisfactory test strategy, with little computational burden.
    Original languageUndefined
    Title of host publicationProceedings of Artifical Intelligence in Medicine Europe (AIME)
    EditorsR Bellazzi, A Abu-Hanna, J Hunter
    Place of PublicationBerlin
    PublisherSpringer
    Pages331-335
    Number of pages5
    DOIs
    Publication statusPublished - 2007
    Event11th Conference on Artificial Intelligence in Medicine, AIME 2007 - Amsterdam, Netherlands
    Duration: 7 Jul 200711 Jul 2007
    Conference number: 11

    Publication series

    NameLecture Notes in Artificial Intelligence
    PublisherSpringer Verlag
    NumberLNCS4549
    Volume4594
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference11th Conference on Artificial Intelligence in Medicine, AIME 2007
    Abbreviated titleAIME
    CountryNetherlands
    CityAmsterdam
    Period7/07/0711/07/07

    Keywords

    • probabilistic networks
    • semi-myopia
    • diagnostic test selection
    • METIS-241771
    • IR-61841
    • EWI-10748

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