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
2019-07-01
1286119 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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