Eibe Frank
Impact in
- Artificial Intelligence top 0.01%
- Text and Document Classification Technologies
- Imbalanced Data Classification Techniques
- Advanced Text Analysis Techniques
- Machine Learning and Data Classification
- Topic Modeling
- Information Systems top 0.01%
- Data Mining Algorithms and Applications
- Spam and Phishing Detection
Papers in
-
- Machine Learning and Data Classification 40
- Text and Document Classification Technologies 18
- Imbalanced Data Classification Techniques 17
- Bayesian Modeling and Causal Inference 13
- Data Stream Mining Techniques 12
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- Data Mining Algorithms and Applications 34
- Co-authors
- Ian H. Witten (56 shared papers)Geoffrey Holmes (32 shared papers)Mark Hall (18 shared papers)Bernhard Pfahringer (39 shared papers)Peter Reutemann (5 shared papers)Jesse Read (3 shared papers)Niels Landwehr (2 shared papers)Mark A. Hall (22 shared papers)
- Journals
- Machine Learning (6 papers)Lecture notes in computer science (39 papers)Chemometrics and Intelligent Laboratory Systems (3 papers)Journal of Machine Learning Research (2 papers)Data Mining and Knowledge Discovery (2 papers)
- Partner nations
- New ZealandCanadaUnited States
In The Last Decade
Eibe Frank
166 papers receiving 46.6k citations
Eibe Frank's Hit Papers
Peers
Comparison fields: 5 of 238
- Artificial Intelligence 25.0k
- Information Systems 12.2k
- Signal Processing 5.1k
- Software 1.4k
- Computer Vision and Pattern Recognition 6.8k
Countries citing papers authored by Eibe Frank
This map shows the geographic impact of Eibe Frank'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 Eibe Frank with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eibe Frank more than expected).
Fields of papers citing papers by Eibe Frank
This network shows the impact of papers produced by Eibe Frank. 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 Eibe Frank. The network helps show where Eibe Frank may publish in the future.
Co-authors
The 25 scholars most cited alongside Eibe Frank, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 169 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The WEKA data mining software Hit paper breakdown → | 2009 | 14775 |
| 2 | Data Mining: Practical Machine Learning Tools and Techniques Hit paper breakdown → | 2011 | 14681 |
| 3 | Data mining Hit paper breakdown → | 2002 | 4321 |
| 4 | Classifier chains for multi-label classification Hit paper breakdown → | 2011 | 1702 |
| 5 | Generating Accurate Rule Sets Without Global Optimization Hit paper breakdown → | 1998 | 1049 |
| 6 | Logistic Model Trees Hit paper breakdown → | 2005 | 991 |
| 7 | Data mining in bioinformatics using Weka Hit paper breakdown → | 2004 | 747 |
| 8 | Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) Hit paper breakdown → | 2005 | 675 |
| 9 | KEA Hit paper breakdown → | 1999 | 650 |
| 10 | Domain-specific keyphrase extraction Hit paper breakdown → | 1999 | 509 |
| 11 | Weka: Practical machine learning tools and techniques with Java implementations Hit paper breakdown → | 1999 | 503 |
| 12 | Classifier Chains for Multi-label Classification Hit paper breakdown → | 2009 | 472 |
| 13 | Sentiment Knowledge Discovery in Twitter Streaming Data Hit paper breakdown → | 2010 | 462 |
| 14 | 2001 | 435 | |
| 15 | 2009 | 349 | |
| 16 | 2005 | 324 | |
| 17 | 2004 | 315 | |
| 18 | 1998 | 314 | |
| 19 | 2004 | 300 | |
| 20 | 2010 | 268 |
About Eibe Frank
Eibe Frank is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Signal Processing, having authored 169 papers that have together received 50.0k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (40 papers), Data Mining Algorithms and Applications (34 papers), Text and Document Classification Technologies (18 papers), Rough Sets and Fuzzy Logic (17 papers), Imbalanced Data Classification Techniques (17 papers), Bayesian Modeling and Causal Inference (13 papers), Image Retrieval and Classification Techniques (13 papers) and Data Stream Mining Techniques (12 papers). The work is most often cited by research in Artificial Intelligence (25.0k citations), Information Systems (12.2k citations), Signal Processing (5.1k citations), Software (1.4k citations) and Computer Vision and Pattern Recognition (6.8k citations). Eibe Frank has collaborated with scholars based in New Zealand, Canada and United States. Frequent co-authors include Ian H. Witten, Geoffrey Holmes, Mark Hall, Bernhard Pfahringer, Peter Reutemann, Jesse Read, Niels Landwehr, Mark A. Hall, Remco Bouckaert and Gordon W. Paynter. Their work appears in journals such as Machine Learning, Lecture notes in computer science, Chemometrics and Intelligent Laboratory Systems, Journal of Machine Learning Research and Data Mining and Knowledge Discovery.
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.