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Research on poverty alleviation performance evaluation method based on Spearman correlation coefficient and entropy weight method

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DOI: 10.23977/ferm.2021.040207 | Downloads: 3 | Views: 237

Author(s)

Yixin Du 1, Zilong Zhang 1

Affiliation(s)

1 Intelligent Equipment College, Shandong university of Science and Technology, Tai'an Shandong, 271000

Corresponding Author

Yixin Du

ABSTRACT

From 2015 to 2020, each supporting unit will carry out accurate poverty alleviation from village to village, but the performance of each poverty alleviation unit is different,If the performance evaluation of each poverty alleviation unit is not reasonable, the enthusiasm and efficiency of poverty alleviation will be affected. In this paper, a new evaluation model is proposed to evaluate each supporting unit, so as to better encourage each supporting unit to improve the efficiency of poverty alleviation, help the poor and help the poor. Firstly, Spearman correlation coefficient is used to measure the relationship between SR, CY, HJ, WJ and SS scores in 2015 and SR, CY, HJ, WJ and SS scores in 2020 . Next, in order to evaluate the performance correctly, this paper gives weight to the changes of village indexes in the past five years by entropy method, and comprehensively evaluates the scores after weighted average, and finally obtains a scientific poverty alleviation performance evaluation method. 

KEYWORDS

precise poverty alleviation, Spearman correlation coefficient, entropy weight method

CITE THIS PAPER

Yixin Du, Zilong Zhang, Research on poverty alleviation performance evaluation method based on Spearman correlation coefficient and entropy weight method. Financial Engineering and Risk Management (2021) 4: 32-35. DOI: http://dx.doi.org/10.23977/ferm.2021.040207

REFERENCES

[1] Guo Yingming, Li Hongli, Research on Weighted Naive Bayesian Classification Algorithm Based on Spearman Coefficient, Information and Computer (Theoretical Edition), No.407 (13): 62-64(2018).
[2] Zhou Ke, Xu Weichao, robustness of Spearman's simple correlation coefficient in Gaussian mixture impulse noise environment, Electronic World, 000 (007): 158-158, 2017. 
[3] Xiong Zhongyang, Chen Ruotian, Zhang Yufang, An Effective k-means Clustering Center Initialization Method, Computer Applied Research, 28(011): 4188-4190, 2011. 

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