A machine learning driven multiple criteria decision analysis using LS-SVM feature elimination: Sustainability performance assessment with incomplete data-Reference-Cited by-同舟云学术

A machine learning driven multiple criteria decision analysis using LS-SVM feature elimination: Sustainability performance assessment with incomplete data

Published:2023-03 Issue: Volume:119 Page:105785
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Ijadi Maghsoodi Abtin,Torkayesh Ali Ebadi^ORCID,Wood Lincoln C.^ORCID,Herrera-Viedma Enrique,Govindan Kannan

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference68 articles.

1. Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation;Alamoodi;Chaos Solitons Fractals,2021

2. Assessing national progress and priorities for the Sustainable Development Goals (SDGs): Experience from Australia;Allen;Sustain. Sci.,2020

3. Estimating incomplete information in group decision making: A framework of granular computing;Cabrerizo;Appl. Soft Comput.,2020

4. A literature-based review on potentials and constraints in the implementation of the Sustainable Development Goals;Caiado;J. Clean. Prod.,2018

5. Fuzzy group decision making with incomplete information guided by social influence;Capuano;IEEE Trans. Fuzzy Syst.,2018

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation;Science of The Total Environment;2024-06

2. A novel interval neutrosophic-based group decision-making approach for sustainable development assessment in the computer manufacturing industry;Engineering Applications of Artificial Intelligence;2024-06

3. Integration of machine learning in the supply chain for decision making: A systematic literature review;Journal of Industrial Engineering and Management;2024-05-14

4. Assessing the current landscape of AI and sustainability literature: identifying key trends, addressing gaps and challenges;Journal of Big Data;2024-05-06

5. Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso;Water Science & Technology;2024-05-01