IDEAS home Printed from https://ideas.repec.org/a/igg/jaec00/v2y2011i1p49-58.html
   My bibliography  Save this article

A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets

Author

Listed:
  • Periasamy Vivekanandan

    (Park College of Engineering and Technology, India)

  • Raju Nedunchezhian

    (Kalaignar Karunanidhi Institute of Technology, India)

Abstract

Genetic algorithm is a search technique purely based on natural evolution process. It is widely used by the data mining community for classification rule discovery in complex domains. During the learning process it makes several passes over the data set for determining the accuracy of the potential rules. Due to this characteristic it becomes an extremely I/O intensive slow process. It is particularly difficult to apply GA when the training data set becomes too large and not fully available. An incremental Genetic algorithm based on boosting phenomenon is proposed in this paper which constructs a weak ensemble of classifiers in a fast incremental manner and thus tries to reduce the learning cost considerably.

Suggested Citation

  • Periasamy Vivekanandan & Raju Nedunchezhian, 2011. "A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 2(1), pages 49-58, January.
    • Handle: RePEc:igg:jaec00:v:2:y:2011:i:1:p:49-58
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jaec.2011010104
    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:49-58. 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.