Unsupervised machine learning approach for building composite indicators with fuzzy metrics
作者：
Highlights：
• Fuzzy metrics allow benchmarking and the assessment of progress towards set targets.
• The composite indicator is computed by using unsupervised machine learning techniques.
• For calculating the importance of single indicators a optimization model is proposed.
• The robustness is checked conducting simulations.
• We fill the gap in traditional methods of building composite indicators.
摘要
•Fuzzy metrics allow benchmarking and the assessment of progress towards set targets.•The composite indicator is computed by using unsupervised machine learning techniques.•For calculating the importance of single indicators a optimization model is proposed.•The robustness is checked conducting simulations.•We fill the gap in traditional methods of building composite indicators.
论文关键词：Machine learning,Fuzzy metric,Composite indicator,Benchmarking,Robustness and sensitivity analysis
论文评审过程：Received 12 April 2020, Revised 4 June 2021, Accepted 15 March 2022, Available online 4 April 2022, Version of Record 5 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116927
