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.
Supervise Learning With Copulas
01.07.2019
1286119 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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