IDEAS home Printed from https://ideas.repec.org/a/igg/jsir00/v8y2017i2p50-66.html
   My bibliography  Save this article

Artificial Bee Colony Optimization for Feature Selection of Traffic Sign Recognition

Author

Listed:
  • Diogo L. da Silva

    (Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife, Brazil & Federal Institute of Pernambuco (IFPE), Palmares, Brazil)

  • Leticia M. Seijas

    (Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina)

  • Carmelo J. A. Bastos-Filho

    (Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife, Brazil)

Abstract

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal.

Suggested Citation

  • Diogo L. da Silva & Leticia M. Seijas & Carmelo J. A. Bastos-Filho, 2017. "Artificial Bee Colony Optimization for Feature Selection of Traffic Sign Recognition," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 8(2), pages 50-66, April.
    • Handle: RePEc:igg:jsir00:v:8:y:2017:i:2:p:50-66
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSIR.2017040104
    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:jsir00:v:8:y:2017:i:2:p:50-66. 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.