A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach.
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| Title: | A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach. |
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| Authors: | Satapathy, Sandeep Kumar1 (AUTHOR), Saravanan, Shreyaa1 (AUTHOR), Mishra, Shruti1 (AUTHOR) shrutim2129@gmail.com, Mohanty, Sachi Nandan2 (AUTHOR) |
| Source: | New Generation Computing. Mar2023, Vol. 41 Issue 1, p155-184. 30p. |
| Subjects: | Observational learning, Multidimensional databases, Poverty, Feature selection, Comparative studies, Random forest algorithms, K-nearest neighbor classification, Machine learning |
| Abstract: | Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R2 score. [ABSTRACT FROM AUTHOR] |
| Copyright of New Generation Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Satapathy%2C+Sandeep+Kumar%22">Satapathy, Sandeep Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Saravanan%2C+Shreyaa%22">Saravanan, Shreyaa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mishra%2C+Shruti%22">Mishra, Shruti</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shrutim2129@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mohanty%2C+Sachi+Nandan%22">Mohanty, Sachi Nandan</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22New+Generation+Computing%22">New Generation Computing</searchLink>. Mar2023, Vol. 41 Issue 1, p155-184. 30p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Observational+learning%22">Observational learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multidimensional+databases%22">Multidimensional databases</searchLink><br /><searchLink fieldCode="DE" term="%22Poverty%22">Poverty</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R2 score. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of New Generation Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00354-023-00203-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 155 Subjects: – SubjectFull: Observational learning Type: general – SubjectFull: Multidimensional databases Type: general – SubjectFull: Poverty Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Comparative studies Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Satapathy, Sandeep Kumar – PersonEntity: Name: NameFull: Saravanan, Shreyaa – PersonEntity: Name: NameFull: Mishra, Shruti – PersonEntity: Name: NameFull: Mohanty, Sachi Nandan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 02883635 Numbering: – Type: volume Value: 41 – Type: issue Value: 1 Titles: – TitleFull: New Generation Computing Type: main |
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