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6 September 2022 Identification model of poverty-prone population and analysis of poverty-causing factors
Yiwen Wu, Huiyu Duan, Jikui Wang
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Proceedings Volume 12332, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022); 123321S (2022) https://doi.org/10.1117/12.2652463
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022), 2022, Chengdu, China
Abstract
At the present stage, China's poverty-returning population identification mainly adopts manual identification methods, and many off-target phenomena have occurred. This paper proposes a Fuzzy C-means clustering (FCM) and random forest model for identifying the poverty-prone population to address this problem. Firstly, a multidimensional poverty identification indicator is established based on the sustainable livelihood indicator system (SLIS). Secondly, the dataset was extracted from the 2018 China Family Panel Studies (CFPS) data based on the SLIS system, and FCM was used to cluster the dataset into poor and non-poor. The high confidence poverty data were extracted as the poverty dataset. The FCM was then used to classify the poverty data into those prone to return to poverty and those who are hard to return to poverty. At the same time, random forest is used to construct an accurate identification model for the easy-to-return population on the poverty dataset. The model was evaluated using evaluation indexes such as accuracy. The results showed that the identification results of the model were consistent with the clustering results of the FCM. Finally, the random forest model was used to analyze the poverty-causing factors of the poverty-prone population data, and the main factors that cause poverty were derived.
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Yiwen Wu, Huiyu Duan, and Jikui Wang "Identification model of poverty-prone population and analysis of poverty-causing factors", Proc. SPIE 12332, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022), 123321S (6 September 2022); https://doi.org/10.1117/12.2652463
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KEYWORDS
Data modeling
Factor analysis
Fuzzy logic
Statistical analysis
Performance modeling
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