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Professor Eibe Frank

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

  • Montiel, J., Mitchell, R., Frank, E., Pfahringer, B., Abdessalem, T., & Bifet, A. (2020). Adaptive XGBoost for evolving data streams. In Proc 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Glasgow, UK: IEEE. doi:10.1109/IJCNN48605.2020.9207555

  • 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. (2020). Transfer of pretrained model weights substantially improves semi-supervised image classification. In M. Gallagher, N. Noustafa, & E. Lakshika (Eds.), AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science Vol. LNAI 12576 (pp. 434-444). Cham: Springer. doi:10.1007/978-3-030-64984-5_34

  • Wang, H., Gouk, H., Frank, E., Pfahringer, B., & Mayo, M. (2020). A comparison of machine learning methods for cross-domain few-shot learning. In M. Gallagher, N. Moustafa, & E. Lakshika (Eds.), AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science Vol. LNAI 12576 (pp. 445-457). Cham: Springer. doi:10.1007/978-3-030-64984-5_35 Open Access version: https://hdl.handle.net/10289/14027

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
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