Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Isolation Forest
20083.1k citationsFei Tony Liu, Kai Ming Ting et al.profile →
Isolation-Based Anomaly Detection
20121.3k citationsFei Tony Liu, Kai Ming Ting et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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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).
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 of co-authors of Kai Ming Ting
This figure shows the co-authorship network connecting the top 25 collaborators of Kai Ming Ting.
A scholar is included among the top collaborators of Kai Ming Ting 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 Kai Ming Ting. Kai Ming Ting is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Fu, Zhouyu, Guojun Lu, Kai Ming Ting, & Dengsheng Zhang. (2013). Optimizing cepstral features for audio classification. FedUni ResearchOnline (Federation University Australia). 1330–1336.3 indexed citations
8.
Wells, Jonathan R., et al.. (2012). A non-time series approach to vehicle related time series problems. FedUni ResearchOnline (Federation University Australia). 61–70.2 indexed citations
Ting, Kai Ming, et al.. (2003). Improving time series prediction by data selection. 803–813.
13.
Ting, Kai Ming. (2002). Issues in Classifier Evaluation using Optimal Cost Curves. International Conference on Machine Learning. 642–649.3 indexed citations
14.
Ting, Kai Ming. (2000). A Comparative Study of Cost-Sensitive Boosting Algorithms. International Conference on Machine Learning. 983–990.175 indexed citations
15.
Zheng, Zijian, Geoffrey I. Webb, & Kai Ming Ting. (1999). Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. International Conference on Machine Learning. 493–502.22 indexed citations
16.
Ting, Kai Ming & Ian H. Witten. (1997). Stacked generalization: when does it work?. Research Commons (University of Waikato). 866–871.86 indexed citations
17.
Ting, Kai Ming & Ian H. Witten. (1997). Stacking Bagged and Dagged Models. Research Commons (The University of Waikato). 367–375.148 indexed citations
18.
Ting, Kai Ming & Ian H. Witten. (1997). Stacked Generalizations: When Does It Work?. International Joint Conference on Artificial Intelligence. 866–873.7 indexed citations
19.
Ting, Kai Ming. (1996). The characterisation of predictive accuracy and decision combination. International Conference on Machine Learning. 498–506.1 indexed citations
20.
Ting, Kai Ming, et al.. (1995). Maximizing tree diversity by building complete-random decision trees.3 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.