What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
The scientist’s investigation covers issues in Evidential reasoning approach, Artificial intelligence, Data mining, Machine learning and Fuzzy logic.
His Evidential reasoning approach research is multidisciplinary, incorporating perspectives in Fuzzy set, Process and Dempster–Shafer theory.
His Artificial intelligence research includes themes of Multiple-criteria decision analysis and Decision matrix.
His work investigates the relationship between Data mining and topics such as Legal expert system that intersect with problems in Pipeline and Test data.
His research integrates issues of Decision theory and Rule-based system in his study of Machine learning.
His studies deal with areas such as Risk management and Operations research as well as Decision analysis.
His most cited work include:
- On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty (640 citations)
- An evidential reasoning approach for multiple-attribute decision making with uncertainty (577 citations)
- Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties (546 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Evidential reasoning approach, Artificial intelligence, Machine learning, Process and Mathematical optimization.
His work deals with themes such as Multiple-criteria decision analysis and Inference, which intersect with Evidential reasoning approach.
As part of his studies on Artificial intelligence, he often connects relevant subjects like Nonlinear programming.
He interconnects Reliability, Decision theory and Expert system in the investigation of issues within Machine learning.
His Process research includes elements of New product development and Operations research.
His study looks at the intersection of Decision support system and topics like Risk analysis with Risk assessment.
He most often published in these fields:
- Evidential reasoning approach (48.79%)
- Artificial intelligence (33.82%)
- Machine learning (24.64%)
What were the highlights of his more recent work (between 2015-2021)?
- Evidential reasoning approach (48.79%)
- Artificial intelligence (33.82%)
- Machine learning (24.64%)
In recent papers he was focusing on the following fields of study:
Jian-Bo Yang mainly investigates Evidential reasoning approach, Artificial intelligence, Machine learning, Process and Operations research.
His research in Evidential reasoning approach intersects with topics in Multiple-criteria decision analysis, Belief distribution and Rule-based system.
He combines subjects such as Risk analysis and Decision analysis with his study of Multiple-criteria decision analysis.
His Artificial intelligence research incorporates elements of Triage and Identification.
His work in Machine learning addresses subjects such as Reliability, which are connected to disciplines such as Group decision-making.
In his study, which falls under the umbrella issue of Process, Quality is strongly linked to Data-driven.
Between 2015 and 2021, his most popular works were:
- A bibliometric analysis of operations research and management science (114 citations)
- Data classification using evidence reasoning rule (87 citations)
- Accounting Research: A Bibliometric Analysis (63 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Evidential reasoning approach, Artificial intelligence, Machine learning, Data mining and Belief distribution are his primary areas of study.
His Evidential reasoning approach study necessitates a more in-depth grasp of Statistics.
He has included themes like Analytic hierarchy process, Survey data collection, Aggregate and Patient experience in his Artificial intelligence study.
His Machine learning study incorporates themes from Business case, Reliability, Decision support system and Patient satisfaction.
His Data mining research is multidisciplinary, incorporating elements of Classifier and Statistic.
Jian-Bo Yang studied Process and Group decision-making that intersect with Decision matrix and Ranking.
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