W.S. Lee
Impact in
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Text and Document Classification Technologies
- Imbalanced Data Classification Techniques
- Machine Learning and Algorithms
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Information Systems top 5%
- Spam and Phishing Detection
Papers in
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- Image and Signal Denoising Methods 2
- Advanced Data Compression Techniques 2
- Advanced Image Processing Techniques 1
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- Machine Learning and Algorithms 1
- Machine Learning and Data Classification 1
- Co-authors
- Xiaoli Li (1 shared paper)Philip S. Yu (1 shared paper)B. Liu (1 shared paper)Ashraf A. Kassim (1 shared paper)Pingkun Yan (1 shared paper)K. Sengupta (1 shared paper)Min Jeong Hong (1 shared paper)So Jin Park (1 shared paper)
- Journals
- Computers and Electronics in Agriculture (1 paper)National University of Singapore (2 papers)IEEE Transactions on Information Technology in Biomedicine (1 paper)
- Partner nations
- SingaporeUnited StatesSouth Korea
In The Last Decade
W.S. Lee
5 papers receiving 563 citations
W.S. Lee's Hit Papers
Peers
Comparison fields: 5 of 77
- Artificial Intelligence 403
- Information Systems 134
- Computer Vision and Pattern Recognition 113
- Signal Processing 47
- Ecological Modeling 12
Countries citing papers authored by W.S. Lee
This map shows the geographic impact of W.S. Lee'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 W.S. Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites W.S. Lee more than expected).
Fields of papers citing papers by W.S. Lee
This network shows the impact of papers produced by W.S. Lee. 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 W.S. Lee. The network helps show where W.S. Lee may publish in the future.
Co-authors
The 10 scholars most cited alongside W.S. Lee, 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 | Building text classifiers using positive and unlabeled examples Hit paper breakdown → | 2004 | 530 |
| 2 | 2005 | 30 | |
| 3 | 1999 | 18 | |
| 4 | 2024 | 8 | |
| 5 | 2023 | 1 |
About W.S. Lee
W.S. Lee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Transportation and Plant Science, having authored 5 papers that have together received 587 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (2 papers), Advanced Data Compression Techniques (2 papers), Advanced Image Processing Techniques (1 paper), Spectroscopy and Chemometric Analyses (1 paper), Machine Learning and Algorithms (1 paper), Machine Learning and Data Classification (1 paper), Video Coding and Compression Technologies (1 paper) and Greenhouse Technology and Climate Control (1 paper). The work is most often cited by research in Artificial Intelligence (403 citations), Information Systems (134 citations), Computer Vision and Pattern Recognition (113 citations), Signal Processing (47 citations) and Ecological Modeling (12 citations). W.S. Lee has collaborated with scholars based in Singapore, United States and South Korea. Frequent co-authors include Xiaoli Li, Philip S. Yu, B. Liu, Ashraf A. Kassim, Pingkun Yan, K. Sengupta, Min Jeong Hong, So Jin Park, Dae-Hyun Jung and Aude Hofleitner. Their work appears in journals such as Computers and Electronics in Agriculture, National University of Singapore and IEEE Transactions on Information Technology in Biomedicine.
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.