Machine Learning Based Approach for Sustainable Social Protection Policies in Developing Societies
Date
2021
Authors
Mumtaz, Zahid
Whiteford, Peter
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Publisher
Baltzer Science Publishers B.V.
Abstract
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.
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Keywords
Artificial intelligence, Machine learning, K-means clustering, Big data, Social protection, Smart and developing societies
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Source
Mobile Networks and Applications
Type
Journal article
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Restricted until
2099-12-31
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