Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

Mehrbakhsh Nilashi, Shahla Asadi, Rabab Ali Abumalloh, Sarminah Samad, Fahad Ghabban, Eko Supriyanto and Reem Osman
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Mehrbakhsh Nilashi: School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
Shahla Asadi: Centre of Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Rabab Ali Abumalloh: Computer Department, Community College, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
Sarminah Samad: Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Fahad Ghabban: Faculty of Computer Science and Engineering, Information System Department, Taibah University, Madinah 41411, Saudi Arabia
Eko Supriyanto: School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
Reem Osman: Computer Department, Community College, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia

Sustainability, 2021, vol. 13, issue 7, 1-24

Abstract: This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

Keywords: Classification and Regression Trees (CART); clustering; decision making; ensemble learning; Self-Organizing Map (SOM); Sustainability Assessment by Fuzzy Evaluation (SAFE); sustainability assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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