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Alpha Skew Gaussian Naïve Bayes Classifier

Department of Statistics, Federal University of Paraná, Av. Coronel Francisco Heráclito dos Santos, 100, Curitiba-PR, PO Box 19081, 81531-980, Brazil

E-mail Address: [email protected]

Corresponding author.

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Francisco Louzada

Institute of Mathematical and Computer Sciences, University of São Paulo, Av. Trabalhador São Carlense, 400, São Carlos-SP, 13566-590, Brazil

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https://doi.org/10.1142/S0219622021500644Cited by:0

Abstract

The main goal of this paper is to introduce a new procedure for a naïve Bayes classifier, namely alpha skew Gaussian naïve Bayes (ASGNB), which is based on a flexible generalization of the Gaussian distribution applied to continuous variables. As a direct advantage, this method can accommodate the possibility to handle with asymmetry in the uni or bimodal behavior. We provide the estimation procedure of this method, and we check the predictive performance when compared to other traditional classification methods using simulation studies and many real datasets with different application fields. The ASGNB is a powerful alternative to classification tasks when lie the presence of asymmetry of bimodality in the data and outperforms well when compared to other traditional classification methods in most of the cases analyzed.

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References

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Vol. 21, No. 01

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History

Received 2 April 2020

Revised 12 August 2021

Accepted 14 August 2021

Published: 12 October 2021

Keywords

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Alpha Skew Gaussian Naïve Bayes Classifier

Abstract

References

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