What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algebra
His scientific interests lie mostly in Fuzzy logic, Preference, Artificial intelligence, Mathematical optimization and Group.
His Fuzzy set study, which is part of a larger body of work in Fuzzy logic, is frequently linked to Preference, bridging the gap between disciplines.
His Preference relation study in the realm of Preference connects with subjects such as Consistency.
His work on Selection as part of general Artificial intelligence research is frequently linked to Weighting, thereby connecting diverse disciplines of science.
His work deals with themes such as Simple, Consistency and Transitive relation, which intersect with Mathematical optimization.
His Group study integrates concerns from other disciplines, such as Self-organizing map and Machine learning.
His most cited work include:
- A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress (170 citations)
- Approaches based on 2-tuple linguistic power aggregation operators for multiple attribute group decision making under linguistic environment (149 citations)
- A consensus model for hesitant fuzzy preference relations and its application in water allocation management (122 citations)
What are the main themes of his work throughout his whole career to date?
Yejun Xu mainly focuses on Fuzzy logic, Preference, Mathematical optimization, Artificial intelligence and Preference.
Yejun Xu has included themes like Multiplicative function, Data mining and Operations research in his Fuzzy logic study.
His study looks at the relationship between Preference and topics such as Consistency, which overlap with Reciprocal.
His studies deal with areas such as Intuitionistic fuzzy, Simple, Algorithm and Complete information as well as Mathematical optimization.
His work is dedicated to discovering how Artificial intelligence, Group are connected with Order and other disciplines.
His work carried out in the field of Fuzzy classification brings together such families of science as Fuzzy number and Defuzzification.
He most often published in these fields:
- Fuzzy logic (46.73%)
- Preference (37.38%)
- Mathematical optimization (25.23%)
What were the highlights of his more recent work (between 2017-2021)?
- Fuzzy logic (46.73%)
- Preference (37.38%)
- Computational intelligence (10.28%)
In recent papers he was focusing on the following fields of study:
Fuzzy logic, Preference, Computational intelligence, Artificial intelligence and Consistency are his primary areas of study.
His Fuzzy logic study which covers Reciprocal that intersects with Preference relation.
To a larger extent, he studies Statistics with the aim of understanding Preference.
In his work, Consensus reaching process, Computation, Discrete mathematics and Pattern recognition is strongly intertwined with Data mining, which is a subfield of Computational intelligence.
The Artificial intelligence study which covers Machine learning that intersects with Matching.
His Consistency study incorporates themes from Multiplicative function, Algorithm and Ordinal number.
Between 2017 and 2021, his most popular works were:
- A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection (100 citations)
- An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions (64 citations)
- Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making (56 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Yejun Xu mainly investigates Fuzzy logic, Preference, Consistency, Reciprocal and Self-confidence.
His Fuzzy logic research integrates issues from Computational intelligence, Data mining and Cluster analysis.
Yejun Xu has researched Computational intelligence in several fields, including Econometrics, Process and Fuzzy linguistic.
His Preference research is multidisciplinary, incorporating elements of Machine learning and Artificial intelligence.
His research integrates issues of Group and Scale in his study of Artificial intelligence.
Yejun Xu works mostly in the field of Consistency, limiting it down to topics relating to Multiplicative function and, in certain cases, Theoretical computer science and Multiplicative consistency, as a part of the same area of interest.
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