Sensitivity of multi-dimensional Bayesian classifiers

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    One-dimensional Bayesian network classifiers (OBCs) are popular
    tools for classification [2]. An OBC is a Bayesian network [4] consisting
    of just a single class variable and several feature variables.
    Multi-dimensional Bayesian network classifiers (MBCs) were introduced
    to generalise OBCs to multiple class variables [1, 6].
    Classification performance of OBCs is known to be rather good.
    Experimental results that support this observation were substantiated
    by a study of the sensitivity properties of naive OBCs [5]. In this
    paper we investigate the sensitivity of MBCs. We present sensitivity
    functions for the outcome probabilities of interest of an MBC and use
    these functions to study the sensitivity value. This value captures the
    sensitivity of an output probability to small changes in a parameter.
    We compare MBCs to OBCs in this respect and conclude that an
    MBC will on average be even more robust to parameter changes than
    an OBC.
    Original languageEnglish
    Title of host publicationProceedings of the 21st European Conference on Artificial Intelligence
    Subtitle of host publication(ECAI)
    EditorsTorsten Schaub, Gerhard Friedrich, Barry O'Sullivan
    PublisherIOS Press
    ISBN (Electronic)978-1-61499-419-0
    ISBN (Print)978-1-61499-418-3
    Publication statusPublished - 2014
    EventECAI 2014 - Prague, Czech Republic
    Duration: 18 Aug 201422 Aug 2014

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    PublisherIOS Press
    ISSN (Print)0922-6389
    ISSN (Electronic)1879-8314


    ConferenceECAI 2014
    Country/TerritoryCzech Republic


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