Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach
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 datase...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/jom/5357997 |
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| author | Shushant Hatwar Yogalakshmi Thangaraj Sujatha Vishnumoorthy |
| author_facet | Shushant Hatwar Yogalakshmi Thangaraj Sujatha Vishnumoorthy |
| author_sort | Shushant Hatwar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8e930ebb6f9c43d1ad454178e1d6fb01 |
| institution | DOAJ |
| issn | 2314-4785 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-8e930ebb6f9c43d1ad454178e1d6fb012025-08-20T03:09:11ZengWileyJournal of Mathematics2314-47852025-01-01202510.1155/jom/5357997Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis ApproachShushant Hatwar0Yogalakshmi Thangaraj1Sujatha Vishnumoorthy2Department of Mechanical EngineeringDepartment of MathematicsDepartment of MathematicsAccurately 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.http://dx.doi.org/10.1155/jom/5357997 |
| spellingShingle | Shushant Hatwar Yogalakshmi Thangaraj Sujatha Vishnumoorthy Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach Journal of Mathematics |
| title | Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach |
| title_full | Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach |
| title_fullStr | Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach |
| title_full_unstemmed | Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach |
| title_short | Addressing Imbalance in Poverty Classification: A SMOTE-Enabled Statistical Analysis Approach |
| title_sort | addressing imbalance in poverty classification a smote enabled statistical analysis approach |
| url | http://dx.doi.org/10.1155/jom/5357997 |
| work_keys_str_mv | AT shushanthatwar addressingimbalanceinpovertyclassificationasmoteenabledstatisticalanalysisapproach AT yogalakshmithangaraj addressingimbalanceinpovertyclassificationasmoteenabledstatisticalanalysisapproach AT sujathavishnumoorthy addressingimbalanceinpovertyclassificationasmoteenabledstatisticalanalysisapproach |