Dimensionality reduction for multi-criteria problems: An application to the decommissioning of oil and gas installations
作者：
Highlights：
• Assessing criteria is essential for decision making in decommissioning.
• Large oil and gas fields require a large number of criteria evaluations.
• We propose feature selection and classification methods for dimensionality reduction.
• The dataset is composed by a reduced set of sub-criteria and parameters.
• Significant reduction in dimension can have little impact on performance.
摘要
•Assessing criteria is essential for decision making in decommissioning.•Large oil and gas fields require a large number of criteria evaluations.•We propose feature selection and classification methods for dimensionality reduction.•The dataset is composed by a reduced set of sub-criteria and parameters.•Significant reduction in dimension can have little impact on performance.
论文关键词：Oil and gas,Decommissioning,Dimensionality reduction,Feature selection,Machine learning,Multi-criteria decision analysis
论文评审过程：Received 3 May 2019, Revised 22 January 2020, Accepted 22 January 2020, Available online 23 January 2020, Version of Record 1 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113236
