当前位置: X-MOL 学术Mobile Netw. Appl. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Machine Learning Based Approach for Sustainable Social Protection Policies in Developing Societies
Mobile Networks and Applications ( IF 3.8 ) Pub Date : 2021-01-07 , DOI: 10.1007/s11036-020-01696-z
Zahid Mumtaz , Peter Whiteford

Machine learning has been increasingly used for making informed public policy decisions, however, its application in the area of social protection in developing societies has been largely overlooked. We have employed unsupervised machine learning K-means clustering technique for exploring a big data that comprised of 88 attributes and 570 instances for better targeting of households that are in urgent need of welfare from the government. The clusters formed showed common patterns relating to insecurities in terms of loss of income and property, unemployment, disasters and disease etc. faced by households in each cluster. We found that households falling in rural areas jurisdictions face severe insecurities compared to other localities and are in urgent need of social protection interventions. We concluded that by employing K-means clustering unsupervised machine learning approach big data (even if it is limited) can be explored effectively for better targeting of social protection interventions for both developing and smart societies. The unsupervised machine learning technique presented in this study is an efficient approach because it can be used by societies that are facing data constraints and can achieve optimal results for increasing the welfare of poor by using the said approach.



中文翻译:

基于机器学习的发展中社会可持续社会保护政策方法

机器学习已越来越多地用于制定明智的公共政策决策,但是,在发展中社会的社会保护领域,机器学习的应用却被大大忽略。我们采用无监督的机器学习K-means聚类技术来探索包含88个属性和570个实例的大数据,以更好地定位急需政府福利的家庭。形成的集群在每个集群的家庭所面临的收入和财产损失,失业,灾难和疾病等方面表现出与不安全感相关的常见模式。我们发现,与其他地区相比,农村地区辖区的家庭面临着严重的不安全感,并且迫切需要社会保护措施。我们得出的结论是,通过采用K-means聚类的无监督机器学习方法,可以有效地探索大数据(即使它是有限的),以更好地针对发展中和智慧社会的社会保护干预措施。这项研究中提出的无监督机器学习技术是一种有效的方法,因为它可以被面临数据约束的社会所使用,并且可以通过使用该方法获得增加贫困人口福利的最佳结果。

更新日期:2021-01-07
down
wechat
bug