IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v13y2017i3p73-90.html
   My bibliography  Save this article

A Directed Acyclic Graph (DAG) Ensemble Classification Model: An Alternative Architecture for Hierarchical Classification

Author

Listed:
  • Esra'a Alshdaifat

    (Department of Computer Science, University of Liverpool, Liverpool, UK)

  • Frans Coenen

    (Department of Computer Science, University of Liverpool, Liverpool, UK)

  • Keith Dures

    (Department of Computer Science, University of Liverpool, Liverpool, UK)

Abstract

In this paper, a hierarchical ensemble classification approach that utilizes a Directed Acyclic Graph (DAG) structure is proposed as a solution to the multi-class classification problem. Two main DAG structures are considered: (i) rooted DAG, and (ii) non-rooted DAG. The main challenges that are considered in this paper are: (i) the successive misclassification issue associated with hierarchical classification, and (i) identification of the starting node within the non-rooted DAG approach. To address these issues the idea is to utilize Bayesian probability values to: select the best starting DAG node, and to dictate whether single or multiple paths should be followed within the DAG structure. The reported experimental results indicated that the proposed DAG structure is more effective than when using a simple binary tree structure for generating a hierarchical classification model.

Suggested Citation

  • Esra'a Alshdaifat & Frans Coenen & Keith Dures, 2017. "A Directed Acyclic Graph (DAG) Ensemble Classification Model: An Alternative Architecture for Hierarchical Classification," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 13(3), pages 73-90, July.
    • Handle: RePEc:igg:jdwm00:v:13:y:2017:i:3:p:73-90
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.2017070104
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jdwm00:v:13:y:2017:i:3:p:73-90. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.