The naïve Bayes classifier plays an important role among the classifiers based on supervised learning, although it requires strong condition on the feature independence assumptions. A measurement for the independency checking in the data preprocessing is necessary to guarantee the effectiveness of the classifier. Copula Theory is a mathematical tool in dependency modeling. In this paper, we recall elements of copulas and introduce a new algorithm to construct multiscale copula estimators which can be used for the independency testing to improve the accuracy of the Naïve Bayes classifier.


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    Titel :

    Supervise Learning With Copulas


    Beteiligte:
    Shen, Xiaoping (Autor:in) / Ewing, Robert L. (Autor:in) / Li, Jia (Autor:in)


    Erscheinungsdatum :

    2019-07-01


    Format / Umfang :

    1286119 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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