World's Best Scientists 2026 revealed!
Bernhard Pfahringer

Bernhard Pfahringer

Award Badge
Computer Science
New Zealand
2026

D-Index & Metrics

Computer Science

D-Index
59
Citations
46691
World Ranking
3313
National Ranking
6

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

Bernhard Pfahringer is affiliated with the University of Waikato in New Zealand and has contributed extensively to the field of Computer Science, focusing primarily on Artificial Intelligence and related subfields. With a total of 143 publications, their research spans several specialized areas.

The main subfields of Bernhard Pfahringer's work include Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computer Networks and Communications, and Information Systems.

Key topics addressed in their research encompass:

  • Data Stream Mining Techniques
  • Machine Learning and Data Classification
  • Anomaly Detection Techniques and Applications
  • Domain Adaptation and Few-Shot Learning
  • Network Security and Intrusion Detection
  • Text and Document Classification Technologies
  • Topic Modeling

Bernhard Pfahringer has published in a range of venues, with frequent contributions to:

  • arXiv (Cornell University)
  • Machine Learning
  • Data Mining and Knowledge Discovery
  • Journal of the Royal Society of New Zealand
  • Zenodo (CERN European Organization for Nuclear Research)

Their recent published papers include:

  • MEKA: A multi-label/multi-target extension to Weka, 2025, Aaltodoc (Aalto University)
  • Regularisation of neural networks by enforcing Lipschitz continuity, 2020, Machine Learning
  • Machine Learning (In) Security: A Stream of Problems, 2023, Digital Threats Research and Practice
  • Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction, 2021, Cognitive Computation
  • A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric?, 2020, Applied Artificial Intelligence

The scientist frequently collaborates with several co-authors including Albert Bifet, Heitor Murilo Gomes, Eibe Frank, Guilherme Weigert Cassales, and Vithya Yogarajan. Among these, Albert Bifet stands out with 39 joint publications, highlighting a significant partnership in research activities.

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

  • 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

  • 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

  • Meta-Learning by Landmarking Various Learning Algorithms

    Bernhard Pfahringer;Hilan Bensusan;Christophe G. Giraud-Carrier

  • 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

  • Regularisation of neural networks by enforcing Lipschitz continuity

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

  • Locally weighted naive bayes

    Eibe Frank;Mark Hall;Bernhard Pfahringer

  • Winning the KDD99 classification cup: bagged boosting

    Bernhard Pfahringer

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

    Jesse Read;Peter Reutemann;Bernhard Pfahringer;Geoff Holmes

  • SMOTE for Regression

    Luís Torgo;Rita Paula Ribeiro;B Pfahringer;Paula Oliveira Branco

  • Multiclass alternating decision trees

    Geoffrey Holmes;Bernhard Pfahringer;Richard Kirkby;Eibe Frank

  • Machine Learning for Data Streams: With Practical Examples in Moa

    Albert Bifet;Ricard Gavaldà;Geoff Holmes;Bernhard Pfahringer

Frequent Co-Authors

Geoffrey Holmes
Geoffrey Holmes University of Waikato
Eibe Frank
Eibe Frank University of Waikato
Albert Bifet
Albert Bifet University of Waikato
Stefan Kramer
Stefan Kramer Johannes Gutenberg University of Mainz
Jesse Read
Jesse Read École Polytechnique
Ricard Gavaldà
Ricard Gavaldà Universitat Politècnica de Catalunya
Francis Bach
Francis Bach École Normale Supérieure
Joaquin Vanschoren
Joaquin Vanschoren Eindhoven University of Technology
Johannes Fürnkranz
Johannes Fürnkranz Johannes Kepler University of Linz

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degrees opens up numerous pathways closely connected to computer science. For those interested in the intersection of law and technology, affordable online criminal justice programs can provide the perfect foundation for cybersecurity or digital forensics careers.

Tech skills are invaluable in the business world, especially for those eyeing positions in finance and analysis. Earning the best online accounting degree combines computer science principles with financial expertise—an edge in today’s data-driven economy.

A data science background is highly sought after in tech industries. You can boost your credentials with a cheap online masters degree in data science, which expands your knowledge of AI, machine learning, and big data analytics.

Lastly, tech innovation is transforming construction. Pursuing an online bachelor's in construction management can lead to roles integrating project management software, automation, and digital collaboration tools.

These diverse online degree pathways complement a computer science background, broadening both your expertise and career opportunities.

Best Scientists Citing Bernhard Pfahringer

Trending Scientists

Recently Published Articles