IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v21y2022i01ns0219622021500644.html
   My bibliography  Save this article

Alpha Skew Gaussian Naïve Bayes Classifier

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anderson Ara & Francisco Louzada, 2022. "Alpha Skew Gaussian Naïve Bayes Classifier," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 441-462, January.
    • Handle: RePEc:wsi:ijitdm:v:21:y:2022:i:01:n:s0219622021500644
    DOI: 10.1142/S0219622021500644
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622021500644
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219622021500644?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijitdm:v:21:y:2022:i:01:n:s0219622021500644. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.