IDEAS home Printed from https://ideas.repec.org/a/vrs/itmasc/v18y2015i1p129-134n20.html
   My bibliography  Save this article

Ontology-Based Classification System Development Methodology

Author

Listed:
  • Grabusts Peter

    (Rezekne Higher Educational Institution)

  • Borisov Arkady
  • Aleksejeva Ludmila

    (Riga Technical University)

Abstract

The aim of the article is to analyse and develop an ontology-based classification system methodology that uses decision tree learning with statement propositionalized attributes. Classical decision tree learning algorithms, as well as decision tree learning with taxonomy and propositionalized attributes have been observed. Thus, domain ontology can be extracted from the data sets and can be used for data classification with the help of a decision tree. The use of ontology methods in decision tree-based classification systems has been researched. Using such methodologies, the classification accuracy in some cases can be improved.

Suggested Citation

  • Grabusts Peter & Borisov Arkady & Aleksejeva Ludmila, 2015. "Ontology-Based Classification System Development Methodology," Information Technology and Management Science, Sciendo, vol. 18(1), pages 129-134, December.
    • Handle: RePEc:vrs:itmasc:v:18:y:2015:i:1:p:129-134:n:20
    DOI: 10.1515/itms-2015-0020
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/itms-2015-0020
    Download Restriction: no
    File URL: https://libkey.io/10.1515/itms-2015-0020?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
    ---><---

    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:vrs:itmasc:v:18:y:2015:i:1:p:129-134:n:20. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.