IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v21y2022i05ns0219622022500304.html
   My bibliography  Save this article

Multi-Criteria Decision-Making-Based Model Selection Proposal in Artificial Learning Process

Author

Listed:
  • Fatma Önay KoçoÄŸlu

    (Software Engineering Department, Muğla SıtkıKoçman University, Kötekli, Muğla 48000, Turkey)

Abstract

In this study, selection of the best classification model in the artificial learning process is considered as a Multi-Criteria Decision-Making problem. In this direction, machine learning- based 10 classification models have been obtained and seven of them have been eliminated according to the different parameters. The classification model with the best performance among three remaining alternatives has been determined by ELECTRE I. According to the model performances obtained within the scope of the study, the best model among the three alternatives would be determined depending on the initiative of the researcher. However, with the proposed model, this process has been moved to a scientific basis and the best of the three models based on Extreme Learning Machine (ELM), Naïve Bayes, and Support Vector Machine has been clearly determined as ELM. The proposed model, unlike its counterparts in the literature, is far from a complex structure, is understandable and can support users of all levels.

Suggested Citation

  • Fatma Önay KoçoÄŸlu, 2022. "Multi-Criteria Decision-Making-Based Model Selection Proposal in Artificial Learning Process," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1467-1486, September.
    • Handle: RePEc:wsi:ijitdm:v:21:y:2022:i:05:n:s0219622022500304
    DOI: 10.1142/S0219622022500304
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622022500304
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219622022500304?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:05:n:s0219622022500304. 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.