Eric C.C. Tsang

6.4k total citations
160 papers, 4.7k citations indexed

About

Eric C.C. Tsang is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Information Systems. According to data from OpenAlex, Eric C.C. Tsang has authored 160 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 101 papers in Artificial Intelligence, 83 papers in Computational Theory and Mathematics and 46 papers in Information Systems. Recurrent topics in Eric C.C. Tsang's work include Rough Sets and Fuzzy Logic (77 papers), Data Mining Algorithms and Applications (41 papers) and Neural Networks and Applications (28 papers). Eric C.C. Tsang is often cited by papers focused on Rough Sets and Fuzzy Logic (77 papers), Data Mining Algorithms and Applications (41 papers) and Neural Networks and Applications (28 papers). Eric C.C. Tsang collaborates with scholars based in China, Hong Kong and Macao. Eric C.C. Tsang's co-authors include Degang Chen, Wing W. Y. Ng, Xizhao Wang, Daniel Yeung, Suyun Zhao, Weihua Xu, Defeng Wang, Yanting Guo, Junhyeong Lee and Meng Hu and has published in prestigious journals such as IEEE Transactions on Signal Processing, Expert Systems with Applications and Renewable Energy.

In The Last Decade

Eric C.C. Tsang

146 papers receiving 4.6k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Eric C.C. Tsang 3.1k 2.3k 1.5k 1.3k 743 160 4.7k
Weihua Xu 3.8k 1.2× 2.2k 1.0× 1.7k 1.1× 1.2k 1.0× 833 1.1× 185 4.8k
William Zhu 3.7k 1.2× 2.2k 1.0× 1.6k 1.1× 872 0.7× 1.0k 1.4× 176 5.4k
Xibei Yang 3.1k 1.0× 2.1k 0.9× 1.6k 1.0× 978 0.8× 716 1.0× 192 4.4k
Degang Chen 6.3k 2.0× 3.4k 1.5× 3.2k 2.1× 2.2k 1.7× 1.1k 1.4× 184 7.5k
Huaxiong Li 2.1k 0.7× 1.3k 0.6× 792 0.5× 1.3k 1.0× 642 0.9× 126 3.3k
Wojciech Ziarko 4.6k 1.5× 2.8k 1.2× 2.6k 1.7× 1.1k 0.9× 590 0.8× 65 5.6k
JingTao Yao 1.4k 0.4× 1.2k 0.6× 714 0.5× 722 0.6× 252 0.3× 133 4.7k
Chuan Luo 2.0k 0.6× 1.4k 0.6× 1.3k 0.8× 501 0.4× 506 0.7× 97 2.8k
Xianzhong Zhou 1.6k 0.5× 922 0.4× 641 0.4× 1.0k 0.8× 250 0.3× 132 2.5k
Yuhua Qian 8.9k 2.8× 5.8k 2.6× 4.8k 3.1× 2.5k 2.0× 2.1k 2.8× 241 11.4k

Countries citing papers authored by Eric C.C. Tsang

Since Specialization
Citations

This map shows the geographic impact of Eric C.C. Tsang'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 Eric C.C. Tsang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric C.C. Tsang more than expected).

Fields of papers citing papers by Eric C.C. Tsang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric C.C. Tsang. 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 Eric C.C. Tsang. The network helps show where Eric C.C. Tsang may publish in the future.

Co-authorship network of co-authors of Eric C.C. Tsang

This figure shows the co-authorship network connecting the top 25 collaborators of Eric C.C. Tsang. A scholar is included among the top collaborators of Eric C.C. Tsang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Eric C.C. Tsang. Eric C.C. Tsang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tsang, Eric C.C., et al.. (2025). Fidelity-Preserving Concept Stylization with ST-LoRA and multimodal conditions. Expert Systems with Applications. 296. 128966–128966.
2.
Tsang, Eric C.C., et al.. (2025). Multi-level correlation information fusion via three-way concept-cognitive learning for multi-label learning. Information Fusion. 124. 103361–103361. 1 indexed citations
3.
Zhang, Yong, et al.. (2025). Dual-branch graph Transformer for node classification. Electronic Research Archive. 33(2). 1093–1119.
4.
Tsang, Eric C.C., et al.. (2024). Correlation concept-cognitive learning model for multi-label classification. Knowledge-Based Systems. 290. 111566–111566. 15 indexed citations
5.
Xu, Xiaowei, Neng Ren, Ziqing Lu, et al.. (2024). A Data-Driven Approach for the Fast Prediction of Macrosegregation. Metallurgical and Materials Transactions A. 55(6). 2083–2097. 5 indexed citations
6.
Zhang, Chengling, et al.. (2023). Dynamic updating variable precision three-way concept method based on two-way concept-cognitive learning in fuzzy formal contexts. Information Sciences. 655. 119818–119818. 14 indexed citations
7.
Tsang, Eric C.C., et al.. (2023). Ensemble of kernel extreme learning machine based elimination optimization for multi-label classification. Knowledge-Based Systems. 278. 110817–110817. 17 indexed citations
8.
Hu, Meng, Eric C.C. Tsang, Yanting Guo, Degang Chen, & Weihua Xu. (2021). Attribute reduction based on overlap degree and k-nearest-neighbor rough sets in decision information systems. Information Sciences. 584. 301–324. 38 indexed citations
9.
Tsang, Eric C.C., et al.. (2017). Improving the lenet with batch normalization and online hard example mining for digits recognition. 149–153. 5 indexed citations
10.
Naterer, G.F., et al.. (2008). Effects of blade configurations on flow distribution and power output of a Zephyr vertical axis wind turbine. International Conference on Energy & Environment. 480–486. 4 indexed citations
11.
Tsang, Eric C.C., Degang Chen, & Daniel Yeung. (2008). Approximations and reducts with covering generalized rough sets. Computers & Mathematics with Applications. 56(1). 279–289. 110 indexed citations
12.
Liu, Bo, Zhifeng Hao, & Eric C.C. Tsang. (2008). Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification. IEEE Transactions on Neural Networks. 19(12). 2044–2052. 46 indexed citations
13.
Chen, Degang, Eric C.C. Tsang, Daniel Yeung, & Xizhao Wang. (2005). The parameterization reduction of soft sets and its applications. Computers & Mathematics with Applications. 49(5-6). 757–763. 468 indexed citations
14.
Chen, Degang, Wen‐Xiu Zhang, Daniel Yeung, & Eric C.C. Tsang. (2005). Rough approximations on a complete completely distributive lattice with applications to generalized rough sets. Information Sciences. 176(13). 1829–1848. 156 indexed citations
15.
Wang, Defeng, Lin Shi, Wing W. Y. Ng, & Eric C.C. Tsang. (2005). Nonlinear Canonical Correlation Analysis of fMRI Signals Using HDR Models. PubMed. 2005. 5896–5899. 3 indexed citations
16.
Ng, Wing W. Y., et al.. (2004). Handling Interaction in Fuzzy Production Rule Reasoning. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 34(5). 1979–1987. 19 indexed citations
17.
Tsang, Eric C.C., et al.. (2001). A general updating rule for discrete hopfield-type neural network with delay. International Joint Conference on Artificial Intelligence. 789–794.
18.
Ng, Wing W. Y., et al.. (2001). A comparative study on heuristic algorithms for generating fuzzy decision trees. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 31(2). 215–226. 102 indexed citations
19.
Ng, Wing W. Y. & Eric C.C. Tsang. (1997). A comparative study on similarity-based fuzzy reasoning methods. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 27(2). 216–227. 57 indexed citations
20.
Ng, Wing W. Y., et al.. (1994). Fuzzy production rule refinement using multilayer perceptrons. 211–217 vol.1. 7 indexed citations

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.

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