Alireza Farhangfar
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Vision and Pattern Recognition top 10%
- Statistics and Probability top 5%
- Signal Processing top 10%
- Co-authors
- Lukasz KurganJennifer DyWitold PedryczSaeed AfsharniaRussell GreinerMartin ZinkevichCsaba SzepesváriRoman Shor
- Topics
- Data Mining Algorithms and Applications (4 papers)Data Quality and Management (2 papers)Machine Learning and Data Classification (2 papers)
- Journals
- Pattern RecognitionIEEE Transactions on Systems Man and Cybernetics - Part A Systems and HumansIFAC-PapersOnLine
- Partner nations
- CanadaUnited StatesIran
In The Last Decade
Alireza Farhangfar
8 papers receiving 450 citations
Peers
Comparison fields: 5 of 88
- Artificial Intelligence 276
- Information Systems 139
- Computer Vision and Pattern Recognition 75
- Statistics and Probability 75
- Signal Processing 66
Countries citing papers authored by Alireza Farhangfar
This map shows the geographic impact of Alireza Farhangfar'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 Alireza Farhangfar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alireza Farhangfar more than expected).
Fields of papers citing papers by Alireza Farhangfar
This network shows the impact of papers produced by Alireza Farhangfar. 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 Alireza Farhangfar. The network helps show where Alireza Farhangfar may publish in the future.
Co-authorship network of co-authors of Alireza Farhangfar
This figure shows the co-authorship network connecting the top 25 collaborators of Alireza Farhangfar. A scholar is included among the top collaborators of Alireza Farhangfar 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 Alireza Farhangfar. Alireza Farhangfar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 6 | |
| 4 | A Fast Way to Produce Optimal Fixed-Depth Decision Trees. | 6 |
| 5 | 250 | |
| 6 | 167 | |
| 7 | 16 | |
| 8 | 29 |
About Alireza Farhangfar
Alireza Farhangfar is a scholar working on Medical Laboratory Technology, Information Systems and Management Science and Operations Research, having authored 8 papers that have together received 478 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (4 papers), Data Quality and Management (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Computational Mathematics (6 citations), Statistics and Probability (75 citations) and Artificial Intelligence (276 citations). Alireza Farhangfar has collaborated with scholars based in Canada, United States and Iran. Frequent co-authors include Lukasz Kurgan, Jennifer Dy, Witold Pedrycz, Saeed Afsharnia, Russell Greiner, Martin Zinkevich, Csaba Szepesvári and Roman Shor. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans and IFAC-PapersOnLine.
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.