Improving the performance of precision poverty alleviation based on big data mining and machine learning

Issue title: Fuzzy model for human autonomous computing in extreme surveillance and it’s applications

Guest editors: Varatharajan Ramachandran

Article type: Research Article

Authors: Wang, Lejie^{; *}

Affiliations: Shandong Technology and Business University, Yantai, Shandong, China

Correspondence: [*] Corresponding author. Lejie Wang, Shandong Technology and Business University, Yantai, Shandong, China. E-mail: wljyttj@126.com.

Abstract: Since the reform began in our country, with the rapid economic growth in recent years, the income level has grown extremely unequal, and it is difficult for the low-income poor to benefit from the rapid economic growth. The most important prerequisite for the fight against poverty is the accurate identification of the causes of poverty. To date, our country has not reached the level of maturity required to accurately study the causes of poverty in various households. However, with the rapid development of Internet technology and big data technology in recent years, the application of large-scale data technology and data extraction algorithms to poverty reduction can identify truly poor households faster and more accurately. Compared with traditional machine learning algorithms, there are no machine storage and technical constraints, can use a large amount of data and rely on multiple data samples.

Keywords: Big data mining, machine learning, precision poverty alleviation, performance improvement

DOI: 10.3233/JIFS-189498

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6617-6628, 2021