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

D-Index & Metrics

Computer Science

D-Index
58
Citations
49987
World Ranking
3513
National Ranking
7

Research.com Recognitions

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

Overview

Geoffrey Holmes is affiliated with the University of Waikato in New Zealand. Their research primarily focuses on computer science, with significant contributions in artificial intelligence, computer vision and pattern recognition, biomedical engineering, analytical chemistry, and biophysics.

The topics central to their work include:

  • Domain Adaptation and Few-Shot Learning
  • Spectroscopy and Chemometric Analyses
  • Machine Learning and Data Classification
  • Spectroscopy Techniques in Biomedical and Chemical Research
  • Explainable Artificial Intelligence (XAI)
  • Adversarial Robustness in Machine Learning
  • Advanced Chemical Sensor Technologies

Holmes has published extensively, with notable papers such as:

  • MEKA: A multi-label/multi-target extension to Weka, 2025, Aaltodoc (Aalto University)
  • GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles, 2022, PeerJ Computer Science
  • Sampling Permutations for Shapley Value Estimation, 2021, arXiv (Cornell University)
  • Quantitative Mineral Mapping of Drill Core Surfaces II: Long-Wave Infrared Mineral Characterization Using μXRF and Machine Learning, 2020, Economic Geology
  • Augmenting NIR Spectra in deep regression to improve calibration, 2023, Chemometrics and Intelligent Laboratory Systems

Their work appears frequently in publication venues including arXiv (Cornell University), Chemometrics and Intelligent Laboratory Systems, IEEE Transactions on NanoBioscience, Journal of the Royal Society of New Zealand, and PeerJ Computer Science.

Holmes collaborates regularly with several researchers, with frequent co-authors being:

  • Eibe Frank
  • Bernhard Pfahringer
  • Rory Mitchell
  • Hongyu Wang
  • Michael Mayo

Best Publications

  • 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

  • MOA: Massive Online Analysis, a framework for stream classification and clustering.

    Albert Bifet;Geoffrey Holmes;Bernhard Pfahringer;Philipp Kranen

  • Benchmarking attribute selection techniques for discrete class data mining

    M.A. Hall;G. Holmes

  • WEKA: a machine learning workbench

    G. Holmes;A. Donkin;I.H. Witten

  • Data mining in bioinformatics using Weka

    Eibe Frank;Mark Hall;Len Trigg;Geoffrey Holmes

  • MOA: Massive Online Analysis

    Albert Bifet;Geoff Holmes;Richard Kirkby;Bernhard Pfahringer

  • Classifier Chains for Multi-label Classification

    Jesse Read;Bernhard Pfahringer;Geoff Holmes;Eibe Frank

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

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

  • Adaptive random forests for evolving data stream classification

    Heitor M. Gomes;Albert Bifet;Jesse Read;Jean Paul Barddal

  • New ensemble methods for evolving data streams

    Albert Bifet;Geoff Holmes;Bernhard Pfahringer;Richard Kirkby

  • Weka-A Machine Learning Workbench for Data Mining

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

  • Multinomial naive bayes for text categorization revisited

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

  • Multi-label Classification Using Ensembles of Pruned Sets

    J. Read;B. Pfahringer;G. Holmes

  • Using Model Trees for Classification

    Eibe Frank;Yong Wang;Stuart Inglis;Geoffrey Holmes

  • Active Learning With Drifting Streaming Data

    Indre Zliobaite;Albert Bifet;Bernhard Pfahringer;Geoffrey Holmes

  • WEKA---Experiences with a Java Open-Source Project

    Remco R. Bouckaert;Eibe Frank;Mark A. Hall;Geoffrey Holmes

  • Leveraging bagging for evolving data streams

    Albert Bifet;Geoff Holmes;Bernhard Pfahringer

  • Generating Rule Sets from Model Trees

    Geoffrey Holmes;Mark Hall;Eibe Frank

  • Meka: a multi-label/multi-target extension to weka

    Jesse Read;Peter Reutemann;Bernhard Pfahringer;Geoff Holmes

Frequent Co-Authors

Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
Eibe Frank
Eibe Frank University of Waikato
Ian H. Witten
Ian H. Witten University of Waikato
Mark Hall
Mark Hall University of Sydney
Joaquin Vanschoren
Joaquin Vanschoren Eindhoven University of Technology
Albert Bifet
Albert Bifet University of Waikato
Alan Brennan
Alan Brennan University of Sheffield
Stephen A. Renshaw
Stephen A. Renshaw University of Sheffield
Visakan Kadirkamanathan
Visakan Kadirkamanathan University of Sheffield

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