Kai Ming Ting
- Artificial Intelligence top 0.1%
- Anomaly Detection Techniques and Applications 41
- Machine Learning and Data Classification 22
- Advanced Clustering Algorithms Research 17
- Data Stream Mining Techniques 11
- Imbalanced Data Classification Techniques 9
- Signal Processing top 0.2%
- Time Series Analysis and Forecasting 14
- Computer Networks and Communications top 0.5%
- Network Security and Intrusion Detection 9
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- Face and Expression Recognition 12
Kai Ming Ting
96 papers receiving 6.8k citations
Hit Papers
Peers
Comparison fields: 5 of 178
- Artificial Intelligence 5.0k
- Signal Processing 1.6k
- Computer Networks and Communications 1.8k
- Computer Vision and Pattern Recognition 1000
- Control and Systems Engineering 761
Countries citing papers authored by Kai Ming Ting
This map shows the geographic impact of Kai Ming Ting'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 Kai Ming Ting with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Ming Ting more than expected).
Fields of papers citing papers by Kai Ming Ting
This network shows the impact of papers produced by Kai Ming Ting. 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 Kai Ming Ting. The network helps show where Kai Ming Ting may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kai Ming Ting, 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 | 2025 | 0 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 2 | |
| 5 | 2021 | 4 | |
| 6 | 2014 | 45 | |
| 7 | Optimizing cepstral features for audio classification | 2013 | 3 |
| 8 | A non-time series approach to vehicle related time series problems | 2012 | 2 |
| 9 | 2012 | 29 | |
| 10 | 2011 | 96 | |
| 11 | 2011 | 61 | |
| 12 | Improving time series prediction by data selection | 2003 | 0 |
| 13 | Issues in Classifier Evaluation using Optimal Cost Curves | 2002 | 3 |
| 14 | A Comparative Study of Cost-Sensitive Boosting Algorithms | 2000 | 175 |
| 15 | Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees | 1999 | 22 |
| 16 | Stacked generalization: when does it work? | 1997 | 86 |
| 17 | Stacked Generalizations: When Does It Work? | 1997 | 7 |
| 18 | Stacking Bagged and Dagged Models | 1997 | 148 |
| 19 | The characterisation of predictive accuracy and decision combination | 1996 | 1 |
| 20 | Maximizing tree diversity by building complete-random decision trees | 1995 | 3 |
About Kai Ming Ting
Kai Ming Ting is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition, having authored 99 papers that have together received 7.0k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (41 papers), Machine Learning and Data Classification (22 papers), Advanced Clustering Algorithms Research (17 papers), Time Series Analysis and Forecasting (14 papers), Face and Expression Recognition (12 papers), Data Stream Mining Techniques (11 papers), Network Security and Intrusion Detection (9 papers) and Imbalanced Data Classification Techniques (9 papers). The work is most often cited by research in Artificial Intelligence (5.0k citations), Signal Processing (1.6k citations) and Computer Networks and Communications (1.8k citations). Kai Ming Ting has collaborated with scholars based in Australia, China and Japan. Frequent co-authors include Zhi‐Hua Zhou, Fei Tony Liu, Ian H. Witten, Ye Zhu, Zhouyu Fu, Guojun Lu, Dengsheng Zhang, Geoffrey I. Webb, Swee Chuan Tan and Yue Zhu. Their work appears in journals such as Machine Learning, Pattern Recognition, IEEE Transactions on Knowledge and Data Engineering, Journal of Artificial Intelligence Research and Knowledge and Information 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.