What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Data mining, Multiple-criteria decision analysis, Artificial intelligence, Analytic hierarchy process and Statistical classification.
His Data mining research is multidisciplinary, incorporating elements of Domain, Ranking and Credit card.
His Multiple-criteria decision analysis research includes elements of Financial risk, TOPSIS, Risk analysis, Credit risk and Decision support system.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Machine learning.
His Machine learning research is multidisciplinary, incorporating perspectives in Algorithm and ELECTRE.
His biological study spans a wide range of topics, including Spearman's rank correlation coefficient, Ensemble learning, Binary classification, Grey relational analysis and Rank correlation.
His most cited work include:
- Evaluation of clustering algorithms for financial risk analysis using MCDM methods (435 citations)
- EVALUATION OF CLASSIFICATION ALGORITHMS USING MCDM AND RANK CORRELATION (387 citations)
- A descriptive framework for the field of data mining and knowledge discovery (309 citations)
What are the main themes of his work throughout his whole career to date?
Yi Peng focuses on Data mining, Artificial intelligence, Machine learning, Analytic hierarchy process and Operations research.
His Data mining study combines topics in areas such as Data science, Multiple-criteria decision analysis and Cluster analysis.
Yi Peng has included themes like Ranking, TOPSIS, Statistical classification and Credit risk in his Multiple-criteria decision analysis study.
Yi Peng combines topics linked to Pattern recognition with his work on Artificial intelligence.
His Machine learning study frequently draws connections to adjacent fields such as Network intrusion detection.
In Analytic hierarchy process, Yi Peng works on issues like Consistency, which are connected to Matrix and Algorithm.
He most often published in these fields:
- Data mining (35.46%)
- Artificial intelligence (23.40%)
- Machine learning (19.86%)
What were the highlights of his more recent work (between 2017-2021)?
- Group decision-making (9.22%)
- Operations research (12.06%)
- Artificial intelligence (23.40%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Group decision-making, Operations research, Artificial intelligence, Data mining and Fuzzy logic.
Within one scientific family, Yi Peng focuses on topics pertaining to Selection under Operations research, and may sometimes address concerns connected to Group, Aggregate and Imbalanced data.
The study incorporates disciplines such as Ranking, Machine learning, Data modeling and Wilcoxon signed-rank test in addition to Artificial intelligence.
His Machine learning study frequently links to adjacent areas such as Classifier.
His study in Data mining is interdisciplinary in nature, drawing from both Feature, Support vector machine, Variety, Series and Integer programming.
He combines subjects such as Cluster analysis, Credit history and Scale with his study of Data science.
Between 2017 and 2021, his most popular works were:
- A Group Decision Making Model for Integrating Heterogeneous Information (156 citations)
- Soft consensus cost models for group decision making and economic interpretations (144 citations)
- Machine learning methods for systemic risk analysis in financial sectors (92 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Yi Peng mainly focuses on Artificial intelligence, Machine learning, Group decision-making, Ranking and China.
He interconnects Decision matrix and Data mining, Measure in the investigation of issues within Artificial intelligence.
His Machine learning study incorporates themes from Data modeling and Classifier.
Yi Peng integrates many fields in his works, including Group decision-making, Context, Function, Soft Costs, Resource and Operations research.
His research in Ranking intersects with topics in Decision engineering, Business decision mapping and Fuzzy logic.
Yi Peng incorporates a variety of subjects into his writings, including China, Systemic risk, Foundation, Finance, Big data and Macro.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
