A two-stage similarity clustering-based large group decision-making method with incomplete probabilistic linguistic evaluation information
出版年份 2020 全文链接
标题
A two-stage similarity clustering-based large group decision-making method with incomplete probabilistic linguistic evaluation information
作者
关键词
-
出版物
SOFT COMPUTING
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2020-05-07
DOI
10.1007/s00500-020-04981-x
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Expected consistency-based emergency decision making with incomplete probabilistic linguistic preference relations
- (2019) Jie Gao et al. KNOWLEDGE-BASED SYSTEMS
- A new method for probabilistic linguistic multi-attribute group decision making: Application to the selection of financial technologies
- (2019) Xiao-Bing Mao et al. APPLIED SOFT COMPUTING
- Multiattribute decision making based on power operators for linguistic intuitionistic fuzzy set using set pair analysis
- (2019) Harish Garg et al. EXPERT SYSTEMS
- Large group emergency decision-making method with linguistic risk appetites based on criteria mining
- (2019) Xuanhua Xu et al. KNOWLEDGE-BASED SYSTEMS
- A distance measure between intuitionistic fuzzy sets and its application in medical diagnosis
- (2018) Minxia Luo et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making
- (2018) Xingli Wu et al. Information Fusion
- The optimization-based aggregation and consensus with minimum-cost in group decision making under incomplete linguistic distribution context
- (2018) Bowen Zhang et al. KNOWLEDGE-BASED SYSTEMS
- Water security evaluation based on the TODIM method with probabilistic linguistic term sets
- (2018) Yixin Zhang et al. SOFT COMPUTING
- A large-group emergency risk decision method based on data mining of public attribute preferences
- (2018) Xuanhua Xu et al. KNOWLEDGE-BASED SYSTEMS
- A linear programming method for multiple criteria decision making with probabilistic linguistic information
- (2017) Huchang Liao et al. INFORMATION SCIENCES
- A Review on Shale Reservoirs as an Unconventional Play - The History, Technology Revolution, Importance to Oil and Gas Industry, and the Development Future
- (2016) Wen LIN ACTA GEOLOGICA SINICA-ENGLISH EDITION
- Consistency-based risk assessment with probabilistic linguistic preference relation
- (2016) Yixin Zhang et al. APPLIED SOFT COMPUTING
- Probabilistic linguistic term sets in multi-attribute group decision making
- (2016) Qi Pang et al. INFORMATION SCIENCES
- A method based on trust model for large group decision-making with incomplete preference information
- (2016) Xuanhua Xu et al. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
- Trust based consensus model for social network in an incomplete linguistic information context
- (2015) Jian Wu et al. APPLIED SOFT COMPUTING
- Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for HFLTSs and their application in qualitative decision making
- (2015) Huchang Liao et al. EXPERT SYSTEMS WITH APPLICATIONS
- Entropy, similarity measure and distance measure of vague soft sets and their relations
- (2013) Chang Wang et al. INFORMATION SCIENCES
- Consensus model for multiple criteria group decision making under intuitionistic fuzzy environment
- (2013) Liyuan Zhang et al. KNOWLEDGE-BASED SYSTEMS
- Hesitant Fuzzy Linguistic Term Sets for Decision Making
- (2011) R. M. Rodriguez et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Group decision making with incomplete fuzzy linguistic preference relations
- (2009) S. Alonso et al. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
- Clustering algorithm for intuitionistic fuzzy sets
- (2008) Zeshui Xu et al. INFORMATION SCIENCES
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started