IDEAS home Printed from https://ideas.repec.org/a/igg/jaec00/v2y2011i1p18-33.html
   My bibliography  Save this article

Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve

Author

Listed:
  • Liau Heng Fui

    (University of Nottingham Malaysia, Malaysia)

  • Dino Isa

    (University of Nottingham Malaysia, Malaysia)

Abstract

Feature selection is crucial to select an “optimized” subset of features from the original feature set based on a certain objective function. In general, feature selection removes redundant or irrelevant data while retaining classification accuracy. This paper proposes a feature selection algorithm that aims to minimize the area under the curve of detection error trade-off (DET) curve. Particle swarm optimization (PSO) is employed to search for the optimal feature subset. The proposed method is implemented in face recognition and iris recognition systems. The result shows that the proposed method is able to find an optimal subset of features that sufficiently describes iris and face images by removing unwanted and redundant features and at the same time improving the classification accuracy in terms of total error rate (TER).

Suggested Citation

  • Liau Heng Fui & Dino Isa, 2011. "Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 2(1), pages 18-33, January.
    • Handle: RePEc:igg:jaec00:v:2:y:2011:i:1:p:18-33
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jaec.2011010102
    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:jaec00:v:2:y:2011:i:1:p:18-33. 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.