IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v180y2007i2p723-737.html
   My bibliography  Save this article

Framework for efficient feature selection in genetic algorithm based data mining

Author

Listed:
  • Sikora, Riyaz
  • Piramuthu, Selwyn

Abstract

No abstract is available for this item.

Suggested Citation

  • Sikora, Riyaz & Piramuthu, Selwyn, 2007. "Framework for efficient feature selection in genetic algorithm based data mining," European Journal of Operational Research, Elsevier, vol. 180(2), pages 723-737, July.
    • Handle: RePEc:eee:ejores:v:180:y:2007:i:2:p:723-737
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(06)00261-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    References listed on IDEAS

    as
    1. Riyaz Sikora & Michael Shaw, 1994. "A Double-Layered Learning Approach to Acquiring Rules for Classification: Integrating Genetic Algorithms with Similarity-Based Learning," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 174-187, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Huaijun Wang & Ruomeng Ke & Junhuai Li & Yang An & Kan Wang & Lei Yu, 2018. "A correlation-based binary particle swarm optimization method for feature selection in human activity recognition," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
    2. Ding‐Wen Tan & William Yeoh & Yee Ling Boo & Soung‐Yue Liew, 2013. "The Impact Of Feature Selection: A Data‐Mining Application In Direct Marketing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(1), pages 23-38, January.
    3. Mohd Afizi Mohd Shukran & Yuk Chung & Wei-Chang Yeh & Noorhaniza Wahid & Ahmad Mujahid Ahmad Zaidi, 2011. "Artificial Bee Colony based Data Mining Algorithms for Classification Tasks," Modern Applied Science, Canadian Center of Science and Education, vol. 5(4), pages 217-217, August.
    4. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 2018. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 20(2), pages 401-416, April.
    5. Unler, Alper & Murat, Alper, 2010. "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, Elsevier, vol. 206(3), pages 528-539, November.
    6. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 0. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 0, pages 1-16.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
    2. Tomasz Korol, 2020. "Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1113-1135.
    3. Davalos, Sergio & Gritta, Richard D. & Adrangi, Bahram, 2007. "Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 46(2).
    4. Tomasz Korol, 2018. "The Implementation of Fuzzy Logic in Forecasting Financial Ratios," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 12(2), June.
    5. Leila Bateni & Farshid Asghari, 2020. "Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 335-348, January.

    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:eee:ejores:v:180:y:2007:i:2:p:723-737. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.