Hui Liu1,2 , Yang Liu This email address is being protected from spambots. You need JavaScript enabled to view it.3 , Ran Zhang1 , Dezheng Liu1 , and Zheng Zhang4
1School of Software, Dalian University of Technology, Dalian, Liaoning, China 2Faculty of Business and Management, Universiti Teknologi MARA Sarawak Branch, Sarawak, Malaysia 3International School, Shenyang Jianzhu University, Shenyang, Liaoning, China 4International School of Information Science Engineering, Dalian University of Technology, Dalian, Liaoning, China
Received: August 12, 2020 Accepted: November 26, 2020 Publication Date: June 1, 2021
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Poverty is a historical problem all over the world. Poverty alleviation targeted to the primary problems faced by the poverty-stricken households in different classes can effectively improve the efficiency of poverty reduction. Owing to the large number of features that reflect the poor situation of poverty-stricken households, it is difficult for traditional methods to accurately analyse the primary problems faced by the poverty-stricken households. In view of the high performance of feature selection methods in dealing with high-dimensional data, a feature selection approach by taking the distribution position of features in each class into consideration is proposed in this paper. We use the Gaussian mixture model to describe the distribution of features in the same dimension, and measure the distribution position of the cluster consists of features in each class according to their Gauss mixture ingredients. Features with significant differences in distribution position between classes are selected, which can effectively represent the characteristics of samples in different classes. According to the experimental results, the proposed method performs well in determining the characteristics of samples in different classes, and can accurately analyze the typical features of poverty-stricken households in different classes, which provides the basis for the design of targeted poverty alleviation strategies.
Keywords: Poverty alleviation; poverty-stricken households; feature selection; Gaussian mixture model
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