Poverty Classification Using Machine Learning: The Case of Jordan

被引:32
作者
Alsharkawi, Adham [1 ]
Al-Fetyani, Mohammad [2 ]
Dawas, Maha [3 ]
Saadeh, Heba [4 ]
Alyaman, Musa [1 ]
机构
[1] Univ Jordan, Dept Mechatron Engn, Amman 11942, Jordan
[2] Xina Technol, Dept Data Sci, Amman 11180, Jordan
[3] Dept Stat, Dept Poverty, Amman 11181, Jordan
[4] Univ Jordan, Dept Comp Sci, Amman 11942, Jordan
关键词
sustainable development goals (SDGs); poverty prediction; data preprocessing; classification algorithms; machine learning; society;
D O I
10.3390/su13031412
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The scope of this paper is focused on the multidimensional poverty problem in Jordan. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of Jordanian households. However, carrying out such surveys is hard, time consuming, and expensive. Machine learning could revolutionize this process. The contribution of this work is the proposal of an original machine learning approach to assess and monitor the poverty status of Jordanian households. This approach takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium. This approach is accurate, inexpensive, and makes poverty identification cheaper and much closer to real-time. Data preprocessing and handling imbalanced data are major parts of this work. Various machine learning classification models are applied. The LightGBM algorithm has achieved the best performance with 81% F1-Score. The final machine learning classification model could transform efforts to track and target poverty across the country. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes for adoption across many domains in both the private sector and government.
引用
收藏
页码:1 / 16
页数:16
相关论文
共 45 条
[1]  
[Anonymous], 2017, Jordan poverty reduction strategy 2013
[2]  
[Anonymous], Geographic Multidimensional vulnerability analysis-Jordan
[3]  
Ayush K., ARXIV200604224
[4]  
Babenko B., ARXIV171106323
[5]  
Bank W., 2004, JORD POV ASS, V2
[6]   MULTIDIMENSIONAL BINARY SEARCH TREES USED FOR ASSOCIATIVE SEARCHING [J].
BENTLEY, JL .
COMMUNICATIONS OF THE ACM, 1975, 18 (09) :509-517
[7]   A Survey of Predictive Modeling on Im balanced Domains [J].
Branco, Paula ;
Torgo, Luis ;
Ribeiro, Rita P. .
ACM COMPUTING SURVEYS, 2016, 49 (02)
[8]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[9]  
Breiman L., 2017, Classification and regression trees, DOI [DOI 10.1201/9781315139470-8, 10.1201/9781315139470, 10.1201/9781315139470-8]
[10]  
Brownlee J, 2018, DEEP LEARNING TIME S