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

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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, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jjmath:v:2025:y:2025:i:1:n:5357997
    DOI: 10.1155/jom/5357997
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    References listed on IDEAS

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    1. Jing Li, 2024. "Area under the ROC Curve has the most consistent evaluation for binary classification," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-28, December.
    2. Sabina Alkire & Usha Kanagaratnam & Ricardo Nogales & Nicolai Suppa, 2022. "Revising the Global Multidimensional Poverty Index: Empirical Insights and Robustness," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 347-384, December.
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