Debo Cheng
Impact in
- Artificial Intelligence top 2%
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Imbalanced Data Classification Techniques
- Text and Document Classification Technologies
- Advanced Graph Neural Networks
- Machine Learning and ELM
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- Face and Expression Recognition
Papers in
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- Advanced Graph Neural Networks 15
- Bayesian Modeling and Causal Inference 10
- Privacy-Preserving Technologies in Data 4
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- Face and Expression Recognition 17
- Co-authors
- Shichao Zhang (25 shared papers)Ming Zong (5 shared papers)Zhenyun Deng (8 shared papers)Xiaofeng Zhu (5 shared papers)Xiaoshu Zhu (1 shared paper)Xuelong Li (1 shared paper)Xuelian Deng (1 shared paper)Rongyao Hu (6 shared papers)
In The Last Decade
Debo Cheng
46 papers receiving 1.4k citations
Debo Cheng's Hit Papers
Peers
Comparison fields: 5 of 154
- Artificial Intelligence 708
- Computer Vision and Pattern Recognition 398
- Health Information Management 63
- Signal Processing 95
- Information Systems 188
Countries citing papers authored by Debo Cheng
This map shows the geographic impact of Debo Cheng'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 Debo Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debo Cheng more than expected).
Fields of papers citing papers by Debo Cheng
This network shows the impact of papers produced by Debo Cheng. 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 Debo Cheng. The network helps show where Debo Cheng may publish in the future.
Co-authors
The 25 scholars most cited alongside Debo Cheng, 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 53 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Efficient kNN classification algorithm for big data Hit paper breakdown → | 2016 | 430 |
| 2 | Learning k for kNN Classification Hit paper breakdown → | 2017 | 364 |
| 3 | 2017 | 192 | |
| 4 | 2016 | 138 | |
| 5 | 2023 | 28 | |
| 6 | 2015 | 22 | |
| 7 | 2023 | 19 | |
| 8 | 2017 | 19 | |
| 9 | 2016 | 17 | |
| 10 | 2020 | 16 | |
| 11 | 2016 | 16 | |
| 12 | 2023 | 15 | |
| 13 | 2024 | 15 | |
| 14 | 2016 | 15 | |
| 15 | 2022 | 12 | |
| 16 | 2023 | 12 | |
| 17 | 2022 | 11 | |
| 18 | 2024 | 10 | |
| 19 | 2016 | 10 | |
| 20 | 2016 | 10 |
About Debo Cheng
Debo Cheng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability, Information Systems and Computational Mechanics, having authored 53 papers that have together received 1.5k indexed citations. Recurring topics across this work include Face and Expression Recognition (17 papers), Advanced Graph Neural Networks (15 papers), Advanced Causal Inference Techniques (10 papers), Bayesian Modeling and Causal Inference (10 papers), Recommender Systems and Techniques (7 papers), Sparse and Compressive Sensing Techniques (7 papers), Statistical Methods and Inference (6 papers) and Privacy-Preserving Technologies in Data (4 papers). The work is most often cited by research in Artificial Intelligence (708 citations), Computer Vision and Pattern Recognition (398 citations), Health Information Management (63 citations), Signal Processing (95 citations) and Information Systems (188 citations). Debo Cheng has collaborated with scholars based in China, Australia and Japan. Frequent co-authors include Shichao Zhang, Ming Zong, Zhenyun Deng, Xiaofeng Zhu, Xiaoshu Zhu, Xuelong Li, Xuelian Deng, Rongyao Hu, Wei He and Yan Yan. Their work appears in journals such as Neurocomputing, Multimedia Tools and Applications, Knowledge-Based Systems, Neural Networks and IEEE Transactions on Neural Networks and Learning 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.