Next Article in Journal
Corporate Social Responsibility (CSR) in the Travel Supply Chain: A Literature Review
Next Article in Special Issue
Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers
Previous Article in Journal
Cross-Sector Partnerships for Innovation and Growth: Can Creative Industries Support Traditional Sector Innovations?
Previous Article in Special Issue
Machine Learning for Conservation Planning in a Changing Climate
Article

Using a DEA–AutoML Approach to Track SDG Achievements

by 1 and 1,2,*
1
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049, China
2
College of Belt and Road, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(23), 10124; https://doi.org/10.3390/su122310124
Received: 22 October 2020 / Revised: 28 November 2020 / Accepted: 30 November 2020 / Published: 4 December 2020
Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval and correlation to demonstrate the approach. Three prediction targets are set to measure performance in the regression, classification, and multi-target regression algorithms. The back-propagation neural network (BPNN) is used to validate the outputs of the AutoML. As a result, AutoML can outperform BPNN for regression and classification prediction problems. Low standard deviation (SD) data result in poor prediction performance for the BPNN, but does not have a significant impact on AutoML. Highly correlated data result in a higher accuracy, but does not significantly affect the R-squared values between the actual and predicted values. This integrative approach can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country. View Full-Text
Keywords: automated machine learning (AutoML); Belt and Road Initiative (BRI); coronavirus disease (COVID-19); data envelopment analysis (DEA); Sustainable Development Goals (SDGs) automated machine learning (AutoML); Belt and Road Initiative (BRI); coronavirus disease (COVID-19); data envelopment analysis (DEA); Sustainable Development Goals (SDGs)
Show Figures

Figure 1

MDPI and ACS Style

Singpai, B.; Wu, D. Using a DEA–AutoML Approach to Track SDG Achievements. Sustainability 2020, 12, 10124. https://doi.org/10.3390/su122310124

AMA Style

Singpai B, Wu D. Using a DEA–AutoML Approach to Track SDG Achievements. Sustainability. 2020; 12(23):10124. https://doi.org/10.3390/su122310124

Chicago/Turabian Style

Singpai, Bodin, and Desheng Wu. 2020. "Using a DEA–AutoML Approach to Track SDG Achievements" Sustainability 12, no. 23: 10124. https://doi.org/10.3390/su122310124

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop