2026 Kai Ming Ting: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	43	7754	7531	1023	1017	316	16424

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Computer Science

7754

7531

1023

1017

316

16424

Overview

Kai Ming Ting is affiliated with Nanjing University in China and has contributed extensively to the field of Computer Science, with a particular focus on Artificial Intelligence. Their research spans multiple subfields including Computer Vision and Pattern Recognition, Signal Processing, Statistical and Nonlinear Physics, and Computer Networks and Communications.

The scientist's main research topics include:

Anomaly Detection Techniques and Applications
Time Series Analysis and Forecasting
Advanced Clustering Algorithms Research
Face and Expression Recognition
Complex Network Analysis Techniques
Network Security and Intrusion Detection
Machine Learning and Data Classification

Kai Ming Ting has published a significant number of papers, with notable recent works that further illustrate their research interests. Selected recent publications include:

Hierarchical clustering that takes advantage of both density-peak and density-connectivity, 2021, Information Systems
Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest, 2021, IEEE Transactions on Geoscience and Remote Sensing
Improving Deep Forest by Screening, 2020, IEEE Transactions on Knowledge and Data Engineering
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities, 2021, Pattern Recognition
The impact of isolation kernel on agglomerative hierarchical clustering algorithms, 2023, Pattern Recognition

The frequent coauthors collaborating with Kai Ming Ting comprise:

Ye Zhu
Gang Li
Jonathan R. Wells
Takashi Washio
Zhi-Hua Zhou

The research has been regularly published in venues such as:

arXiv (Cornell University)
IEEE Transactions on Knowledge and Data Engineering
Journal of Artificial Intelligence Research
Information Systems
Pattern Recognition

Best Publications

Isolation Forest

F.T. Liu;Kai Ming Ting;Zhi-Hua Zhou

5708
Citations
Isolation-Based Anomaly Detection

Fei Tony Liu;Kai Ming Ting;Zhi-Hua Zhou

2075
Citations
Issues in stacked generalization

Kai Ming Ting;Ian H. Witten

922
Citations
An instance-weighting method to induce cost-sensitive trees

Kai Ming Ting

625
Citations
A Survey of Audio-Based Music Classification and Annotation

Zhouyu Fu;Guojun Lu;Kai Ming Ting;Dengsheng Zhang

602
Citations
A Comparative Study of Cost-Sensitive Boosting Algorithms

Kai Ming Ting

368
Citations
Stacking Bagged and Dagged Models

Kai Ming Ting;Ian H. Witten

284
Citations
Stacked generalization: when does it work?

Kai Ming Ting;Ian H. Witten

231
Citations
Fast anomaly detection for streaming data

Swee Chuan Tan;Kai Ming Ting;Tony Fei Liu

229
Citations
Precision and Recall.

Kai Ming Ting

196
Citations
z-SVM: an SVM for improved classification of imbalanced data

Tasadduq Imam;Kai Ming Ting;Joarder Kamruzzaman

186
Citations
On detecting clustered anomalies using SCiForest

Fei Tony Liu;Kai Ming Ting;Zhi-Hua Zhou

149
Citations
Density-ratio based clustering for discovering clusters with varying densities

Ye Zhu;Kai Ming Ting;Mark J. Carman

146
Citations
Isolation-based anomaly detection using nearest-neighbor ensembles

Tharindu R. Bandaragoda;Kai Ming Ting;David W. Albrecht;Fei Tony Liu

144
Citations
Inducing Cost-Sensitive Trees via Instance Weighting

Kai Ming Ting

140
Citations
Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees

Xin Mu;Kai Ming Ting;Zhi-Hua Zhou

139
Citations
On the application of ROC analysis to predict classification performance under varying class distributions

Geoffrey I. Webb;Kai Ming Ting

135
Citations
Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Geoffrey I. Webb;Janice R. Boughton;Fei Zheng;Kai Ming Ting

119
Citations
Spectrum of variable-random trees

Fei Tony Liu;Kai Ming Ting;Yang Yu;Zhi-Hua Zhou

102
Citations
Multi-Label Learning with Emerging New Labels

Yue Zhu;Kai Ming Ting;Zhi-Hua Zhou

85
Citations

Frequent Co-Authors

Zhi-Hua Zhou Nanjing University

Geoffrey I. Webb Monash University

Guojun Lu Federation University Australia

Gholamreza Haffari Monash University

Ian H. Witten University of Waikato

Yilong Yin Shandong University

Yang Yu Nanjing University

Wei Fan Tencent (China)

Gleb Beliakov Deakin University

Tat-Jun Chin University of Adelaide

External Links

Personal website of Kai Ming Ting Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

If you're considering a future in Computer Science, a range of related online degrees and flexible pathways can expand your career options. Pursuing a data science degree is increasingly popular, offering high earning potential and demand across tech industries without breaking the bank.

Another exciting field is Electrical Engineering. Graduates from the online electrical engineering career outcomes see diverse opportunities in industries ranging from robotics to communications, often with significant growth prospects.

Not all pathways require traditional four-year degrees. In fact, there are numerous certifications that pay well which can be completed quickly and can give your resume a substantial boost—especially in specialized IT roles.

For those looking to further their education fast, the quickest cheapest masters degree programs offer accelerated and affordable options to advance your expertise and earning power, often in just a year.