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

Engineering Applications of Artificial Intelligence, vol. 119, pp. 105785

Authors 5

1. Abtin Ijadi Maghsoodi

   University of Auckland · Health New Zealand

   Affiliation as printed

   Department of Information Systems and Operations Management, Faculty of Business and Economics, Business School, University of Auckland, Auckland 1010, New Zealand

   Department of Intelligence and Insights, Te Whatu Ora Health New Zealand Waikato District, Hamilton 3240, New Zealand

2. Ali Ebadi Torkayesh corresponding Aachen School of Business and Economics

   RWTH Aachen University

   Affiliation as printed

   School of Business and Economics, RWTH Aachen University, 52072 Aachen, Germany

3. Lincoln Christopher Wood

   University of Otago · Curtin University

   Affiliation as printed

   Department of Management, University of Otago, New Zealand

   School of Management, Curtin University, Western Australia, Australia

4. Enrique Herrera‐Viedma

   Universidad de Granada · King Abdulaziz University

   Affiliation as printed

   Andalusian Research Institute in Data Science and Computational Intelligence, Department of Computer Science and AI, University of Granada, 18071 Granada, Spain

   Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia

5. Kannan Govindan

   Yonsei University · University of Southern Denmark · Woxsen School of Business · Shanghai Maritime University

   Affiliation as printed

   Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, Danish Institute for Advanced Study, University of Southern Denmark, Campusvej 55, Odense M, Denmark

   China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai, 201306, China

   School of Business, Woxsen University, Sadasivpet, Telangana, India

   Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea