Suping Xu
Impact in
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- Rough Sets and Fuzzy Logic
- Artificial Intelligence top 5%
- Text and Document Classification Technologies
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
Papers in
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- Text and Document Classification Technologies 6
- Imbalanced Data Classification Techniques 3
- Fuzzy Logic and Control Systems 3
- Machine Learning and Data Classification 2
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- Rough Sets and Fuzzy Logic 9
- Co-authors
- Xibei Yang (16 shared papers)Hualong Yu (3 shared papers)Jingyu Yang (2 shared papers)Eric C.C. Tsang (2 shared papers)Dong‐Jun Yu (1 shared paper)Hengrong Ju (9 shared papers)Lin Shang (6 shared papers)Shaozi Li (1 shared paper)
In The Last Decade
Suping Xu
19 papers receiving 407 citations
Peers
Comparison fields: 5 of 48
- Computational Theory and Mathematics 226
- Artificial Intelligence 293
- Information Systems 164
- Computer Vision and Pattern Recognition 117
- Signal Processing 38
Countries citing papers authored by Suping Xu
This map shows the geographic impact of Suping Xu'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 Suping Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suping Xu more than expected).
Fields of papers citing papers by Suping Xu
This network shows the impact of papers produced by Suping Xu. 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 Suping Xu. The network helps show where Suping Xu may publish in the future.
Co-authors
The 25 scholars most cited alongside Suping Xu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 139 | |
| 2 | 2014 | 48 | |
| 3 | 2021 | 38 | |
| 4 | 2020 | 34 | |
| 5 | 2016 | 23 | |
| 6 | 2021 | 21 | |
| 7 | 2012 | 15 | |
| 8 | 2019 | 13 | |
| 9 | 2016 | 12 | |
| 10 | 2023 | 12 | |
| 11 | 2017 | 11 | |
| 12 | 2021 | 10 | |
| 13 | 2022 | 10 | |
| 14 | 2024 | 7 | |
| 15 | 2024 | 6 | |
| 16 | 2015 | 5 | |
| 17 | 2021 | 3 | |
| 18 | 2025 | 1 | |
| 19 | 2025 | 1 | |
| 20 | 2026 | 0 |
About Suping Xu
Suping Xu is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Computer Vision and Pattern Recognition and Signal Processing, having authored 21 papers that have together received 409 indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (9 papers), Text and Document Classification Technologies (6 papers), Data Mining Algorithms and Applications (5 papers), Imbalanced Data Classification Techniques (3 papers), Fuzzy Logic and Control Systems (3 papers), Music and Audio Processing (3 papers), Machine Learning and Data Classification (2 papers) and Face and Expression Recognition (2 papers). The work is most often cited by research in Computational Theory and Mathematics (226 citations), Artificial Intelligence (293 citations), Information Systems (164 citations), Computer Vision and Pattern Recognition (117 citations) and Signal Processing (38 citations). Suping Xu has collaborated with scholars based in China, Canada and Macao. Frequent co-authors include Xibei Yang, Hualong Yu, Jingyu Yang, Eric C.C. Tsang, Dong‐Jun Yu, Hengrong Ju, Lin Shang, Shaozi Li, Zhihui Liu and Rongrong Ji. Their work appears in journals such as IEEE Transactions on Fuzzy Systems, International Journal of Computational Intelligence Systems, Applied Intelligence, Information Sciences and Knowledge-Based Systems.
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.