Yap Bee Wah
- Artificial Intelligence top 5%
- Sociology and Political Science top 5%
- Accounting top 5%
- Information Systems top 5%
- Marketing top 5%
- Co-authors
- C. H. SimAzlinah MohamedMichael W. BerryS. H. OngT. RamayahRuhaila MaskatMaryam Khanian NajafabadiWan Fairos Wan Yaacob
- Topics
- Imbalanced Data Classification Techniques (16 papers)Advanced Statistical Methods and Models (8 papers)Machine Learning and Data Classification (6 papers)
- Partner nations
- MalaysiaUnited StatesMacao
In The Last Decade
Yap Bee Wah
73 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 194
- Artificial Intelligence 375
- Sociology and Political Science 273
- Accounting 247
- Information Systems 183
- Marketing 181
Countries citing papers authored by Yap Bee Wah
This map shows the geographic impact of Yap Bee Wah'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 Yap Bee Wah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yap Bee Wah more than expected).
Fields of papers citing papers by Yap Bee Wah
This network shows the impact of papers produced by Yap Bee Wah. 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 Yap Bee Wah. The network helps show where Yap Bee Wah may publish in the future.
Co-authorship network of co-authors of Yap Bee Wah
This figure shows the co-authorship network connecting the top 25 collaborators of Yap Bee Wah. A scholar is included among the top collaborators of Yap Bee Wah 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 Yap Bee Wah. Yap Bee Wah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 11 | |
| 6 | 9 | |
| 7 | 0 | |
| 8 | 5 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 95 | |
| 12 | 5 | |
| 13 | Feature selection methods: case of filter and wrapper approaches for maximising classification accuracy | 92 |
| 14 | 0 | |
| 15 | 1 | |
| 16 | 10 | |
| 17 | 4 | |
| 18 | Comparisons of various types of normality testsbreakdown → | 591 |
| 19 | 19 | |
| 20 | Using data mining predictive models to classify credit card applicants | 17 |
About Yap Bee Wah
Yap Bee Wah is a scholar working on Statistics and Probability, Accounting and Artificial Intelligence, having authored 81 papers that have together received 2.1k indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (16 papers), Advanced Statistical Methods and Models (8 papers) and Machine Learning and Data Classification (6 papers). The work is most often cited by research in Accounting (247 citations), Marketing (181 citations) and Health Information Management (76 citations). Yap Bee Wah has collaborated with scholars based in Malaysia, United States and Macao. Frequent co-authors include C. H. Sim, Azlinah Mohamed, Michael W. Berry, S. H. Ong, T. Ramayah, Ruhaila Maskat, Maryam Khanian Najafabadi, Wan Fairos Wan Yaacob, Ruhaya Atan and Simon Fong. Their work appears in journals such as Scientific Reports, Expert Systems with Applications and Quality of Life Research.
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.