Tzu-Tsung Wong
- Artificial Intelligence top 2%
- Molecular Biology
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Co-authors
- Chun‐Nan HsuKuan-Liang Liu
- Topics
- Bayesian Methods and Mixture Models (9 papers)Bayesian Modeling and Causal Inference (5 papers)Imbalanced Data Classification Techniques (4 papers)
- Journals
- SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsPattern Recognition
- Partner nations
- TaiwanUnited States
In The Last Decade
Tzu-Tsung Wong
19 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 194
- Artificial Intelligence 593
- Molecular Biology 207
- Biomedical Engineering 196
- Computer Vision and Pattern Recognition 191
- Electrical and Electronic Engineering 157
Countries citing papers authored by Tzu-Tsung Wong
This map shows the geographic impact of Tzu-Tsung Wong'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 Tzu-Tsung Wong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tzu-Tsung Wong more than expected).
Fields of papers citing papers by Tzu-Tsung Wong
This network shows the impact of papers produced by Tzu-Tsung Wong. 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 Tzu-Tsung Wong. The network helps show where Tzu-Tsung Wong may publish in the future.
Co-authorship network of co-authors of Tzu-Tsung Wong
This figure shows the co-authorship network connecting the top 25 collaborators of Tzu-Tsung Wong. A scholar is included among the top collaborators of Tzu-Tsung Wong 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 Tzu-Tsung Wong. Tzu-Tsung Wong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 7 | |
| 3 | Weighted Random Forests for Evaluating Financial Credit Risk | 3 |
| 4 | Reliable Accuracy Estimates from k-Fold Cross Validationbreakdown → | 656 |
| 5 | 130 | |
| 6 | 59 | |
| 7 | 7 | |
| 8 | Performance evaluation of classification algorithms by k-fold and leave-one-out cross validationbreakdown → | 1035 |
| 9 | 13 | |
| 10 | 6 | |
| 11 | 40 | |
| 12 | 18 | |
| 13 | 11 | |
| 14 | 11 | |
| 15 | 30 | |
| 16 | 36 | |
| 17 | 26 | |
| 18 | Why Discretization Works for Naive Bayesian Classifiers | 34 |
| 19 | 97 |
About Tzu-Tsung Wong
Tzu-Tsung Wong is a scholar working on Statistics and Probability, Artificial Intelligence and Information Systems, having authored 19 papers that have together received 2.2k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (9 papers), Bayesian Modeling and Causal Inference (5 papers) and Imbalanced Data Classification Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (593 citations), Health Informatics (22 citations) and Health Information Management (63 citations). Tzu-Tsung Wong has collaborated with scholars based in Taiwan and United States. Frequent co-authors include Chun‐Nan Hsu and Kuan-Liang Liu. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Pattern Recognition.
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.