IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v42y2015i8p1677-1689.html
   My bibliography  Save this article

Estimating the minority class proportion with the ROC curve using Military Personality Inventory data of the ROK Armed Forces

Author

Listed:
  • Meesun Sun
  • Kwanghyun Choi
  • Sungzoon Cho

Abstract

The Republic of Korea Armed Forces includes maladjusted conscripts such as the mentally ill, the suicidal, the imprisoned, and those determined by the military commander to be maladjusted. To counteract these problems, it is necessary to identify the maladjusted conscripts to determine who among them would qualify for exemption from active military service or need special attention. We use the Military Personality Inventory (MPI) to make this prediction. Such a prediction presents a kind of class imbalance and class overlap problem, where the majority fulfil active service and the minority are maladjusted, the latter being discharged early from active service. Therefore, most classification algorithms are likely to show low classification performance. As an alternative, this study demonstrates the effective utilization of the receiver operating characteristics curve using MPI data to estimate the maladjusted proportion of persons sharing similar MPI test results. We confirm that the suggested method performs well using the real-world MPI data set. The suggested method is very useful to estimate the proportion of conscripts maladjusted to military life and can help in the management of such persons subject to conscription.

Suggested Citation

  • Meesun Sun & Kwanghyun Choi & Sungzoon Cho, 2015. "Estimating the minority class proportion with the ROC curve using Military Personality Inventory data of the ROK Armed Forces," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1677-1689, August.
  • Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1677-1689
    DOI: 10.1080/02664763.2015.1005060
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2015.1005060
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/02664763.2015.1005060?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Pablo Mart�nez-Camblor & Carlos Carleos & Norberto Corral, 2011. "Powerful nonparametric statistics to compare k independent ROC curves," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1317-1332, May.
    2. Haobo Ren & Xiao-Hua Zhou & Hua Liang, 2004. "A Flexible Method for Estimating the ROC Curve," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 773-784.
    Full references (including those not matched with items on IDEAS)

    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. Arís Fanjul-Hevia & Wenceslao González-Manteiga, 2018. "A comparative study of methods for testing the equality of two or more ROC curves," Computational Statistics, Springer, vol. 33(1), pages 357-377, March.
    2. Pablo Martínez-Camblor & Sonia Pérez-Fernández & Susana Díaz-Coto, 2021. "Optimal classification scores based on multivariate marker transformations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(4), pages 581-599, December.
    3. Fanjul-Hevia, Arís & González-Manteiga, Wenceslao & Pardo-Fernández, Juan Carlos, 2021. "A non-parametric test for comparing conditional ROC curves," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

    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:taf:japsta:v:42:y:2015:i:8:p:1677-1689. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.