IDEAS home Printed from https://ideas.repec.org/a/ids/ijbisy/v31y2019i1p1-26.html
   My bibliography  Save this article

A study of feature selection techniques for predicting customer retention in telecommunication sector

Author

Listed:
  • E. Sivasankar
  • J. Vijaya

Abstract

Feature selection is the process of eliminating irrelevant features from the dataset, while maintaining acceptable classification accuracy. The selected features play an important role which can directly influence the effectiveness of the resulting classification. In this paper, a methodology is proposed consisting of two phases, attributes selection and classification based on the attributes selected. Phase one uses a filter and wrapper method for attribute selection with random over-sampling (Ros) through which the size of attributes set and misclassification error can be reduced. In the second phase, the selected attributes are taken as inputs by classification techniques like decision trees (DT), K-nearest neighbour (KNN), support vector machine (SVM), naive Bayes (NB) and artificial neural network (ANN). Finally, true churn, false churn, specificity and accuracy are measured to evaluate the efficiency of the proposed system and it is found that the above mentioned methodology performs well ahead for churn prediction and suits well for the telecommunication sector.

Suggested Citation

  • E. Sivasankar & J. Vijaya, 2019. "A study of feature selection techniques for predicting customer retention in telecommunication sector," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 31(1), pages 1-26.
    • Handle: RePEc:ids:ijbisy:v:31:y:2019:i:1:p:1-26
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=99524
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hong Pan & Hanxun Zhou, 2020. "Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce," Electronic Commerce Research, Springer, vol. 20(2), pages 297-320, June.
    2. Sunita Dhote & Chandan Vichoray & Rupesh Pais & S. Baskar & P. Mohamed Shakeel, 2020. "Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce," Electronic Commerce Research, Springer, vol. 20(2), pages 259-274, June.

    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:ids:ijbisy:v:31:y:2019:i:1:p:1-26. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=172 .

    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.