IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-41862-5_121.html

A Survey on Major Classification Algorithms and Comparative Analysis of Few Classification Algorithms on Contact Lenses Data Set Using Data Mining Tool

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • Syed Nawaz Pasha

    (S R Engineering College, Department of CSE)

  • D. Ramesh

    (S R Engineering College, Department of CSE)

  • Mohammad Sallauddin

    (S R Engineering College, Department of CSE)

Abstract

With the data being immensely distributed and the need to analyze the data, data mining has gained importance over the years. The data is analyzed to make some strategic decisions and to derive some patterns out of it. Classification algorithms are classical data mining models to excerpt knowledge from bulk amount of data. The focus of the work is on comparison of various decision tree classification algorithms using WEKA tool taking contact lenses dataset. The methods used for classifier comparison are accuracy, mean absolute error and root mean squared error. The outputs are captured using training data set and then compared to understand the accuracy of the classifiers.

Suggested Citation

  • Syed Nawaz Pasha & D. Ramesh & Mohammad Sallauddin, 2020. "A Survey on Major Classification Algorithms and Comparative Analysis of Few Classification Algorithms on Contact Lenses Data Set Using Data Mining Tool," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1201-1209, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_121
    DOI: 10.1007/978-3-030-41862-5_121
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:spr:sprchp:978-3-030-41862-5_121. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.