Identification of relative poverty based on Lasso-based robust Mahalanobis-Taguchi system

CHEN Wenhe, CHENG Longsheng, CHANG Zhipeng, ZHOU Hanting

Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (2) : 527-544.

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Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (2) : 527-544. DOI: 10.12011/SETP2021-0328

Identification of relative poverty based on Lasso-based robust Mahalanobis-Taguchi system

  • CHEN Wenhe1, CHENG Longsheng1, CHANG Zhipeng2,3, ZHOU Hanting1
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Abstract

After 2020, the focus of China's poverty alleviation work will turn to solving the problem of relative poverty. However, the premise and basis of solving relative poverty is to identify relative poverty. Due to the characteristics of relative poverty, such as "high noise, imbalance, relativity and multi-dimension", it is difficult to apply the method of relative poverty identification with single income dimension. Therefore, the Lasso-based robust Mahalanobis-Taguchi system (Lasso-RMTS) is constructed to identify relative poverty. Through the integration of Lasso, robust Mahalanobis distance (RMD), and Mahalanobis-Taguchi system (MTS), this method can not only identify the poverty data of imbalance and relative comparability, but also reduce the dimension and noise of the relative poverty data. At the same time, the RMD can increase the sensitivity of relative poverty data by transforming it into the poverty alleviation index (PAI). The data shows that Lasso-RMTS can accurately and efficiently identify relative poverty, and its performance is better than MTS and other traditional methods of classification.

Key words

Mahalanobis-Taguchi system / poverty alleviation index / identification of relative poverty / Lasso / robust Mahalanobis distance / sustainable livelihood framework

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CHEN Wenhe , CHENG Longsheng , CHANG Zhipeng , ZHOU Hanting. Identification of relative poverty based on Lasso-based robust Mahalanobis-Taguchi system. Systems Engineering - Theory & Practice, 2022, 42(2): 527-544 https://doi.org/10.12011/SETP2021-0328

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Funding

National Natural Science Foundation of China (71673001); Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX21_0356); Key Project of Excellent Young Talents Support Program for Colleges and Universities of Anhui Province (gxyqZD2017040); The Open Fund of Key Laboratory of Anhui Higher Education Institutes (CS2020-ZD02); Key Grant Project of Humanities and Social Science Foundation of the Higher Education Institutions of Anhui Province (SK2021ZD0034)
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