Liangjun Yu
Impact in
- Artificial Intelligence top 5%
- Bayesian Modeling and Causal Inference
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Text and Document Classification Technologies
- Computer Science Applications top 10%
Papers in
-
- Bayesian Modeling and Causal Inference 10
- Text and Document Classification Technologies 2
- Imbalanced Data Classification Techniques 1
- Advanced Text Analysis Techniques 1
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- Rough Sets and Fuzzy Logic 8
- Co-authors
- Liangxiao Jiang (8 shared papers)Dianhong Wang (4 shared papers)Lungan Zhang (4 shared papers)Huan Zhang (2 shared papers)Wenqiang Xu (1 shared paper)Yu Chen (2 shared papers)
- Journals
- Pattern Recognition (2 papers)Neural Computing and Applications (1 paper)IEEE Access (1 paper)Information Sciences (1 paper)Mathematics (2 papers)
- Partner nations
- China
In The Last Decade
Liangjun Yu
10 papers receiving 381 citations
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 285
- Computer Science Applications 27
- Health Information Management 21
- Information Systems 102
- Computational Theory and Mathematics 56
Countries citing papers authored by Liangjun Yu
This map shows the geographic impact of Liangjun Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Liangjun Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liangjun Yu more than expected).
Fields of papers citing papers by Liangjun Yu
This network shows the impact of papers produced by Liangjun Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Liangjun Yu. The network helps show where Liangjun Yu may publish in the future.
Co-authors
The 6 scholars most cited alongside Liangjun Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 157 | |
| 2 | 2020 | 74 | |
| 3 | 2019 | 69 | |
| 4 | 2018 | 28 | |
| 5 | 2017 | 23 | |
| 6 | 2020 | 19 | |
| 7 | 2018 | 15 | |
| 8 | 2021 | 11 | |
| 9 | 2021 | 5 | |
| 10 | 2018 | 1 |
About Liangjun Yu
Liangjun Yu is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Management Science and Operations Research and Statistics and Probability, having authored 10 papers that have together received 402 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (10 papers), Rough Sets and Fuzzy Logic (8 papers), Data Mining Algorithms and Applications (6 papers), Text and Document Classification Technologies (2 papers), Imbalanced Data Classification Techniques (1 paper), Multi-Criteria Decision Making (1 paper), Advanced Text Analysis Techniques (1 paper) and Advanced Statistical Methods and Models (1 paper). The work is most often cited by research in Artificial Intelligence (285 citations), Computer Science Applications (27 citations), Health Information Management (21 citations), Information Systems (102 citations) and Computational Theory and Mathematics (56 citations). Liangjun Yu has collaborated with scholars based in China. Frequent co-authors include Liangxiao Jiang, Dianhong Wang, Lungan Zhang, Huan Zhang, Wenqiang Xu and Yu Chen. Their work appears in journals such as Pattern Recognition, Neural Computing and Applications, IEEE Access, Information Sciences and Mathematics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.