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Professor Eibe Frank

Professor (Computer Science)

Qualifications: Dipl-Inf Karlsruhe PhD Waikato

Contact Details

Email: eibe@waikato.ac.nz
Room: G.2.18
Phone: +64 7 838 4396
Extension: 4396
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

  • 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

  • Gouk, H., Pfahringer, B., & Frank, E. (2019). Stochastic gradient trees. In W. S. Lee, & T. Suzuki (Eds.), Proc 11th Asian Conference on Machine Learning (ACML 2019) Vol. PMLR 101 (pp. 1094-1109). Nagoya, Japan: PMLR. Retrieved from http://proceedings.mlr.press/v101/gouk19a.html

  • 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

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