IDEAS home Printed from https://ideas.repec.org/a/hin/jjmath/5357997.html
   My bibliography  Save this article

Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach

Author

Listed:
  • Shushant Hatwar
  • Yogalakshmi Thangaraj
  • Sujatha Vishnumoorthy

Abstract

Accurately assessing poverty is vital for policy development and growth planning. Using data from the NITI Aayog-India Multinational Poverty Index Progress Review 2023, this study assesses how sophisticated statistical techniques and data-balancing procedures handle difficulties in imbalanced datasets for poverty detection. For resolving imbalances, important techniques include the Huber regressor, Theil–Sen estimator, canonical correlation analysis (CCA), logistic regression, and SMOTE. While CCA identified important determinants of poverty, SMOTE significantly improved the accuracy of logistic regression. The Theil–Sen estimator fought off outliers, while the Huber regressor successfully handled extreme data. The results highlight the value of improved models for classifying poverty in order to facilitate focused initiatives to reduce it.

Suggested Citation

  • Shushant Hatwar & Yogalakshmi Thangaraj & Sujatha Vishnumoorthy, 2025. "Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach," Journal of Mathematics, Hindawi, vol. 2025, pages 1-12, March.
    • Handle: RePEc:hin:jjmath:5357997
    DOI: 10.1155/jom/5357997
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/jmath/2025/5357997.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/jmath/2025/5357997.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/jom/5357997?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
    ---><---

    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:hin:jjmath:5357997. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.