IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i20p2564-d655237.html
   My bibliography  Save this article

Attribute Selecting in Tree-Augmented Naive Bayes by Cross Validation Risk Minimization

Author

Listed:
  • Shenglei Chen

    (Department of E-Commerce, Nanjing Audit University, Nanjing 211815, China)

  • Zhonghui Zhang

    (School of Finance, Nanjing Audit University, Nanjing 211815, China)

  • Linyuan Liu

    (Department of E-Commerce, Nanjing Audit University, Nanjing 211815, China)

Abstract

As an important improvement to naive Bayes, Tree-Augmented Naive Bayes (TAN) exhibits excellent classification performance and efficiency since it allows that every attribute depends on at most one other attribute in addition to the class variable. However, its performance might be lowered as some attributes might be redundant. In this paper, we propose an attribute Selective Tree-Augmented Naive Bayes (STAN) algorithm which builds a sequence of approximate models each involving only the top certain attributes and searches the model to minimize the cross validation risk. Five different approaches to ranking the attributes have been explored. As the models can be evaluated simultaneously in one pass learning through the data, it is efficient and can avoid local optima in the model space. The extensive experiments on 70 UCI data sets demonstrated that STAN achieves superior performance while maintaining the efficiency and simplicity.

Suggested Citation

  • Shenglei Chen & Zhonghui Zhang & Linyuan Liu, 2021. "Attribute Selecting in Tree-Augmented Naive Bayes by Cross Validation Risk Minimization," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2564-:d:655237
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/20/2564/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/20/2564/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdulilah Mohammad Mayet & Seyed Mehdi Alizadeh & Zana Azeez Kakarash & Ali Awadh Al-Qahtani & Abdullah K. Alanazi & Hala H. Alhashimi & Ehsan Eftekhari-Zadeh & Ehsan Nazemi, 2022. "Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime," Mathematics, MDPI, vol. 10(10), pages 1-13, May.

    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:gam:jmathe:v:9:y:2021:i:20:p:2564-:d:655237. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.