World's Best Scientists 2026 revealed!
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Computer Science
New Zealand
2026

D-Index & Metrics

Computer Science

D-Index
65
Citations
140917
World Ranking
2373
National Ranking
4

Research.com Recognitions

  • 2026 - Research.com Computer Science in New Zealand Leader Award
  • 2025 - Research.com Computer Science in New Zealand Leader Award
  • 2023 - Research.com Computer Science in New Zealand Leader Award
  • 2022 - Research.com Computer Science in New Zealand Leader Award

Overview

Eibe Frank is a researcher affiliated with the University of Waikato in New Zealand, specializing primarily in Computer Science with a focus on Artificial Intelligence. Their research spans multiple subfields including Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism, Rehabilitation, and Periodontics.

The main topics covered in their work include Machine Learning and Data Classification, Domain Adaptation and Few-Shot Learning, Diabetic Foot Ulcer Assessment and Management, Adversarial Robustness in Machine Learning, Explainable Artificial Intelligence (XAI), Data Stream Mining Techniques, and Machine Learning and Algorithms.

Recent papers authored or co-authored by Eibe Frank are:

  • Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation (2020, arXiv (Cornell University))
  • Regularisation of neural networks by enforcing Lipschitz continuity (2020, Machine Learning)
  • The DFUC 2020 Dataset: Analysis Towards Diabetic Foot Ulcer Detection (2021, touchREVIEWS in Endocrinology)
  • GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles (2022, PeerJ Computer Science)
  • Methods for Eliciting Informative Prior Distributions: A Critical Review (2022, Decision Analysis)

Eibe Frank frequently collaborates with several researchers including Bernhard Pfahringer, Geoffrey Holmes, Ian H. Witten, Christopher Pal, and James R. Foulds. Their collaborations have contributed to multiple publications distributed across several venues.

The most common publication venues for Eibe Frank are:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Machine Learning
  • Journal of the Royal Society of New Zealand
  • SSRN Electronic Journal

Best Publications

  • Data mining: practical machine learning tools and techniques with Java implementations

    Ian H. Witten;Eibe Frank

  • Data Mining: Practical Machine Learning Tools and Techniques

    Ian H. Witten;Eibe Frank;Mark A. Hall

  • The WEKA data mining software: an update

    Mark Hall;Eibe Frank;Geoffrey Holmes;Bernhard Pfahringer

  • Classifier chains for multi-label classification

    Jesse Read;Bernhard Pfahringer;Geoff Holmes;Eibe Frank

  • Generating Accurate Rule Sets Without Global Optimization

    Eibe Frank;Ian H. Witten

  • Logistic Model Trees

    Niels Landwehr;Mark Hall;Eibe Frank

  • Logistic model trees

    Niels Landwehr;Mark Hall;Eibe Frank

  • KEA: practical automatic keyphrase extraction

    Ian H. Witten;Gordon W. Paynter;Eibe Frank;Carl Gutwin

  • Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

    Ian H. Witten;Eibe Frank

  • Data mining in bioinformatics using Weka

    Eibe Frank;Mark Hall;Len Trigg;Geoffrey Holmes

  • Classifier Chains for Multi-label Classification

    Jesse Read;Bernhard Pfahringer;Geoff Holmes;Eibe Frank

  • Domain-specific keyphrase extraction

    Eibe Frank;Gordon W. Paynter;Ian H. Witten;Carl Gutwin

  • Weka: Practical machine learning tools and techniques with Java implementations

    Ian H. Witten;Eibe Frank;Leonard E. Trigg;Mark A. Hall

  • Sentiment knowledge discovery in twitter streaming data

    Albert Bifet;Eibe Frank

  • Weka-A Machine Learning Workbench for Data Mining

    Eibe Frank;Mark A. Hall;Geoffrey Holmes;Richard Kirkby

  • A Simple Approach to Ordinal Classification

    Eibe Frank;Mark Hall

  • Multinomial naive bayes for text categorization revisited

    Ashraf M. Kibriya;Eibe Frank;Bernhard Pfahringer;Geoffrey Holmes

  • Using Model Trees for Classification

    Eibe Frank;Yong Wang;Stuart Inglis;Geoffrey Holmes

  • Gene selection from microarray data for cancer classification-a machine learning approach

    Yu Wang;Igor V. Tetko;Mark A. Hall;Eibe Frank

  • Evaluating the replicability of significance tests for comparing learning algorithms

    Remco R. Bouckaert;Eibe Frank

  • Regularisation of neural networks by enforcing Lipschitz continuity

    Henry Gouk;Eibe Frank;Bernhard Pfahringer;Michael J. Cree

Frequent Co-Authors

Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
Ian H. Witten
Ian H. Witten University of Waikato
Geoffrey Holmes
Geoffrey Holmes University of Waikato
Stefan Kramer
Stefan Kramer Johannes Gutenberg University of Mainz
Chris Pal
Chris Pal Polytechnique Montréal
Remco R. Bouckaert
Remco R. Bouckaert University of Auckland
Albert Bifet
Albert Bifet University of Waikato
Carl Gutwin
Carl Gutwin University of Saskatchewan
Jesse Read
Jesse Read École Polytechnique
Saif M. Mohammad
Saif M. Mohammad National Research Council Canada

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