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
- Web Data Mining and Analysis
Papers in
-
- Advanced Data Compression Techniques 2
- Image and Signal Denoising Methods 2
- Co-authors
- Xiaoli Li (1 shared paper)Y. Dai (1 shared paper)Philip S. Yu (1 shared paper)B. Liu (1 shared paper)Ashraf Ali Kassim (1 shared paper)K. Sengupta (1 shared paper)Pingkun Yan (1 shared paper)Min Jeong Hong (1 shared paper)
- Journals
- Computers and Electronics in Agriculture (1 paper)IEEE Transactions on Information Technology in Biomedicine (1 paper)National University of Singapore (2 papers)Smart Agricultural Technology (1 paper)
- Partner nations
- SingaporeUnited StatesSouth Korea
In The Last Decade
W.S. Lee
5 papers receiving 570 citations
W.S. Lee's Hit Papers
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 406
- Information Systems 136
- Computer Vision and Pattern Recognition 112
- 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 15 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 | 532 |
| 2 | 2005 | 30 | |
| 3 | 1999 | 16 | |
| 4 | 2024 | 12 | |
| 5 | 2023 | 1 | |
| 6 | 2026 | 0 |
About W.S. Lee
W.S. Lee is a scholar working on Computer Vision and Pattern Recognition, Transportation, Analytical Chemistry, Signal Processing and Artificial Intelligence, having authored 6 papers that have together received 591 indexed citations. Recurring topics across this work include Advanced Data Compression Techniques (2 papers), Image and Signal Denoising Methods (2 papers), Machine Learning and Data Classification (1 paper), Spectroscopy and Chemometric Analyses (1 paper), Text and Document Classification Technologies (1 paper), Data Stream Mining Techniques (1 paper), Bayesian Methods and Mixture Models (1 paper) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Artificial Intelligence (406 citations), Information Systems (136 citations), Computer Vision and Pattern Recognition (112 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, Y. Dai, Philip S. Yu, B. Liu, Ashraf Ali Kassim, K. Sengupta, Pingkun Yan, Min Jeong Hong, So Jin Park and Dae-Hyun Jung. Their work appears in journals such as Computers and Electronics in Agriculture, IEEE Transactions on Information Technology in Biomedicine, National University of Singapore and Smart Agricultural Technology.
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.