Professor Eibe Frank
Professor (Computer Science)
Qualifications: Dipl-Inf Karlsruhe PhD Waikato
Personal Website: http://www.cs.waikato.ac.nz/~eibe/
Research Interests
Professor Frank is a computer scientist whose primary area of interest is machine learning and its applications. He is a core developer of the WEKA machine learning software and has more than 100 publications on machine learning methods and their application to data mining, text mining, and areas of research outside computer science.
Recent Publications
Mayo, M., & Frank, E. (2020). Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data. Applied Artificial Intelligence, 34(6), 484--514. doi:10.1080/08839514.2020.1726615 Open Access version: https://hdl.handle.net/10289/12514
Sahito, A., Frank, E., & Pfahringer, B. (2019). Semi-supervised learning using Siamese networks. In J. Liu, & J. Bailey (Eds.), Proc 32nd Australasian Joint Conference on Advances in Artificial Intelligence (AI 2019), LNCS 11919 (pp. 586-597). Cham: Springer. doi:10.1007/978-3-030-35288-2_47
Bravo-Marquez, F., Frank, E., Pfahringer, B., & Mohammad, S. M. (2019). AffectiveTweets: a Weka package for analyzing affect in tweets. Journal of Machine Learning Research, 20, 1-6. Retrieved from http://jmlr.org/papers/v20/18-450.html Open Access version: https://hdl.handle.net/10289/12617
Leathart, T., Frank, E., Pfahringer, B., & Holmes, G. (2019). On calibration of nested dichotomies. In Q. Yang, Z. -H. Zhou, Z. Gong, M. -L. Zhang, & S. -J. Huang (Eds.), Advances in Knowledge Discovery and Data Mining: Proc 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), LNCS 11439 Vol. Part I (pp. 69-80). Cham: Springer. doi:10.1007/978-3-030-16148-4_6
Find more research publications by Eibe Frank
Keywords
Data mining; Machine Learning
Contact Details
| Name | Extn. | Username | Room | Department | ||||
|---|---|---|---|---|---|---|---|---|
| Frank, Prof Eibe | 4396 | eibe | G.2.18 | Computer Science |
You can contact staff by:
- Calling +64 7 838 4466 select option 1, then enter the extension
- Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
- For extensions starting with 4: dial +64 7 838 extension
- For extensions starting with 5: dial +64 7 858 extension
- For extensions starting with 9: dial +64 7 837 extension
- For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123
- Emailing username@waikato.ac.nz
- Using the campus map to locate their room