As a new interpretable model, three-way decision has also received academic attention in machine learning. With respect to different hesitant fuzzy information, this book deeply discusses the deduction process of decision rules of three-way decision and generates interpretable knowledge with the risk semantics. It further explores the applications of three-way decision to support healthcare management. This book is used as a reference for engineers, technicians, and researchers who are working in the fields of management science, operation management, computer science, information management, fuzzy mathematics, business intelligence, and other fields. It also serves as a textbook for postgraduate and senior undergraduate students of the relevant professional institutions of higher learning.
Series
Language
Place of publication
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
7 s/w Abbildungen, 22 farbige Abbildungen
IX, 179 p. 29 illus., 22 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 11 mm
Weight
ISBN-13
978-3-031-45503-2 (9783031455032)
DOI
10.1007/978-3-031-45501-8
Schweitzer Classification
Decui Liang is currently a research professor with the School of Management and Economics, University of Electronic Science and Technology of China. He has elected as IET Fellow, BCS Fellow, RSA Fellow, IEEE Senior Member, World's Top 2% Scientists 2020-2023, Top 100,000 scientists 2021 in the world. He received the B.S. degree in information management and information system and the Ph.D. degree in management science and engineering from Southwest Jiaotong University, China, in 2008 and 2013, respectively. In 2012, he was a visiting Ph.D. student with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. His research interests include three-way decisions, multiple-attribute decision-making with uncertainty, risk analysis and management, social business analysis, text mining and so on.
Mingwei Wang is currently working as an assistant professor at Xidian University. He received his Ph.D. from the School of Management and Economics at University of Electronic Science and Technology of China in 2023. As a visiting Ph.D. student, he joined the University Research Priority Program on "Social Networks" at the University of Zurich in Switzerland from 2021 to 2022 for research. His research interests include collective intelligence, group decision-making, collective behavior, human-algorithm interactions and business intelligence. He has published 20 papers in leading journals and conference in the fields of management and information.
Zeshui Xu received the Ph.D. degree in management science and engineering from Southeast University, Nanjing, China, in 2003. From October 2005 to December 2007, he was a Postdoctoral Researcher with School of Economics and Management, Tsinghua University, China. He was a Distinguished Young Scholar of the National Natural Science Foundation of China and the Chang Jiang Scholar of the Ministry of Education of China. He is currently a Chair Professor with Sichuan University, Chengdu. He has been elected as the member of AE, EASA, and IASCYS, the Distinguished Fellow of IETI, the Fellow of IEEE, IFSA, RSA, IET, ORS, BCS, IAAM, AAIA, AIIA, ACIS and VEBLEO. He is ranked 30th in 2019 single year scientific impact and 162th in career scientific impact among World's top 100,000 Scientists, and ranked 40th among the World's best scientists in the arena of Computer Science in 2023. He has published 23 monographs by Springer and contributed more than 900 SCI/SSCI articles to professional journals. He is among the world's top 1% most highly cited researchers with more than 94,000 citations in Google Scholar, his h-index is 152. He is currently the Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, Information Sciences, Artificial Intelligence Review, Journal of the Operational Research, Fuzzy Optimization and Decision Making, etc. His current research interests include Decision-making theory and methodology, optimization algorithms, information fusion, and big data analytics.
