Sustainability performance assessment using self-organizing maps (SOM) and classification and ensembles of regression trees (CART)

• Main Authors: Nilashi, Mehrbakhsh, Asadi, Shahla, Abumalloh, Rabab Ali, Samad, Sarminah, Ghabban, Fahad, Supriyanto, Eko, Osman, Reem
• Format: Article
• Language: English
• Published: MDPI AG 2021
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/97427/1/EkoSupriyanto2021_SustainabilityPerformanceAssessmentUsingSelfOrganizingMaps.pdf; http://eprints.utm.my/id/eprint/97427/; http://dx.doi.org/10.3390/su13073870

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.