Alpha Skew Gaussian Naïve Bayes Classifier

Anderson Ara and Francisco Louzada
Additional contact information
Anderson Ara: Department of Statistics, Federal University of Paraná, Av. Coronel Francisco Heráclito dos Santos, 100, Curitiba-PR, PO Box 19081, 81531-980, Brazil
Francisco Louzada: Institute of Mathematical and Computer Sciences, University of São Paulo, Av. Trabalhador São Carlense, 400, São Carlos-SP, 13566-590, Brazil

International Journal of Information Technology & Decision Making (IJITDM), 2022, vol. 21, issue 01, 441-462

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.

Keywords: Naïve Bayes; classifier; Gaussian; skewness; bimodal (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622021500644
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:21:y:2022:i:01:n:s0219622021500644

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622021500644

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().