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
Holmes, G., Frank, E., Fletcher, D., & Sterling, C. (2022). Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models. In Proc 27th International Conference on Intelligent User Interfaces (IUI '22) (pp. 584-593). New York, NY, USA: ACM. doi:10.1145/3490099.3511110
Mitchell, R., Stokes, D., Frank, E., & Holmes, G. (2022). Bandwidth-optimal random shuffling for GPUs. ACM Transactions on Parallel Computing, 9(1). doi:10.1145/3505287
Falconer, J. R., Frank, E., Polaschek, D. L. L., & Joshi, C. (2022). Methods for eliciting informative prior distributions: A critical review. Decision Analysis. doi:10.1287/deca.2022.0451
Mitchell, R., Frank, E., & Holmes, G. (2022). GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles. PeerJ Computer Science, 8, e880. doi:10.7717/peerj-cs.880 Open Access version: https://hdl.handle.net/10289/14815
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Keywords
Data mining; Machine Learning