A preference-approval structure-based non-additive three-way group consensus decision-making approach for medical diagnosis
J Ye, B Sun, J Bai, Q Bao, X Chu, K Bao - Information Fusion, 2024 - Elsevier
The clinical diagnosis decision-making process of integrated traditional Chinese medicine
and Western medicine is essentially a type of group decision-making (GDM) problem, which …
and Western medicine is essentially a type of group decision-making (GDM) problem, which …
[HTML][HTML] Measuring efficiency of retrieval algorithms with Schweizer-Sklar information aggregation
The task of an information retrieval system (IRS) is to retrieve relevant information from large
datasets efficiently. However, selecting the best algorithm for an IRS is a complex decision …
datasets efficiently. However, selecting the best algorithm for an IRS is a complex decision …
A two-stage adaptive consensus reaching model by virtue of three-way clustering for large-scale group decision making
Y Shen, X Ma, J Zhan - Information Sciences, 2023 - Elsevier
As digitalization advances and societal patterns evolve, an increasing number of experts are
becoming integral to the decision-making process. The realm of large-scale group decision …
becoming integral to the decision-making process. The realm of large-scale group decision …
Consensus reaching process using personalized modification rules in large-scale group decision-making
L Guo, J Zhan, G Kou - Information Fusion, 2024 - Elsevier
Managing complex decision-making scenarios often hinges on the effectiveness of large-
scale group decision-making (LSGDM). When confronted with a significant number of …
scale group decision-making (LSGDM). When confronted with a significant number of …
Selection strategy of uniform expert evaluation scale in group decision making
M Zhao, Y Wang, X Meng, X Gou - Journal of the Operational …, 2024 - Taylor & Francis
The scoring habits of experts are embodied in expert evaluation scale (EES). We show that
many inaccurate consensus and ranking results can occur in group decision making (GDM) …
many inaccurate consensus and ranking results can occur in group decision making (GDM) …
Three-way clustering: Foundations, survey and challenges
Clustering, as an unsupervised data mining technique, allows us to classify similar objects
into the same cluster according to certain criteria. It helps us identify patterns between …
into the same cluster according to certain criteria. It helps us identify patterns between …
A novel quantum Dempster's rule of combination for pattern classification
H He, F Xiao - Information Sciences, 2024 - Elsevier
Dempster's rule of combination (DRC) is widely used for uncertainty reasoning in intelligent
information system, which is generalized to complex domain recently. However, as the …
information system, which is generalized to complex domain recently. However, as the …
Fuzzy granular anomaly detection using Markov random walk
Fuzzy information granulation is an important mathematical model in the theory of granular
computing that can effectively handle fuzzy or uncertain information. To address the …
computing that can effectively handle fuzzy or uncertain information. To address the …
A novel clustering method with consistent data in a three-dimensional graphical format over existing clustering mechanisms
M Salman - Information Sciences, 2023 - Elsevier
With regard to scientific literature reviews, these papers are divided into three major aspects:
traditional and novel clustering algorithms, applications, and problems. This study aimed to …
traditional and novel clustering algorithms, applications, and problems. This study aimed to …
Improved interval type-2 fuzzy K-means clustering based on adaptive iterative center with new defuzzification method
X Zhang, T Zhang, Y Zhang, F Ma - International Journal of Approximate …, 2023 - Elsevier
Abstract Interval Type-2 Fuzzy K-means (IT2FKM) is an efficient clustering algorithm, which
mainly focuses on further describes the uncertainty of the data sets by introducing interval …
mainly focuses on further describes the uncertainty of the data sets by introducing interval …