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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

  • Compton, R., Frank, E., Patros, P., & Koay, A. (2020). Embedding Java classes with code2vec: improvements from variable obfuscation [Accepted]. In IEEE/ACM 17th International Conference on Mining Software Repositories (MSR 2020). Seoul, Republic of Korea. doi:10.1145/3379597.3387445 Open Access version: https://hdl.handle.net/10289/13618

  • 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

  • Lang, S., Bravo-Marquez, F., Beckham, C., Hall, M., & Frank, E. (2019). WekaDeeplearning4j: A deep learning package for weka based on Deeplearning4j. Knowledge-Based Systems. doi:10.1016/j.knosys.2019.04.013

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