World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
43
Citations
25896
World Ranking
5973
National Ranking
75

Overview

Pierre Geurts is affiliated with the University of Liège in Belgium and has a focused research profile primarily within the domain of Computer Science. Their research output covers various subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Cognitive Neuroscience, and Molecular Biology.

The main topics of Pierre Geurts' research include:

  • Machine Learning and Data Classification
  • Neural Networks and Applications
  • Domain Adaptation and Few-Shot Learning
  • Aesthetic Perception and Analysis
  • Explainable Artificial Intelligence (XAI)
  • Music and Audio Processing
  • Diverse Musicological Studies

Frequent co-authors collaborating with Pierre Geurts are:

  • Walter Daelemans
  • Mike Kestemont
  • Vân Anh Huynh-Thu
  • Karine Lasaracina
  • Matthia Sabatelli

The scientist has published notably in these venues:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Machine Learning
  • Open Repository and Bibliography (University of Liège)
  • Data Mining and Knowledge Discovery

Recent publications include:

  • "Can local explanation techniques explain linear additive models?" (2023) in Data Mining and Knowledge Discovery
  • "Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain" (2021) in Electronic Imaging
  • "Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species" (2023) in Biomolecules
  • "Optimizing model-agnostic random subspace ensembles" (2023) in Machine Learning
  • "From global to local MDI variable importances for random forests and when they are Shapley values" (2021) in arXiv (Cornell University)

In addition to articles and conference papers, Pierre Geurts has contributed to book literature with a publication titled Artificial Intelligence and Machine Learning released by Springer Science+Business Media in 2020.

Best Publications

  • Extremely randomized trees

    Pierre Geurts;Damien Ernst;Louis Wehenkel

  • SCENIC: single-cell regulatory network inference and clustering.

    Sara Aibar;Carmen Bravo González-Blas;Thomas Moerman;Vân Anh Huynh-Thu

  • Inferring Regulatory Networks from Expression Data Using Tree-Based Methods

    Vân Anh Huynh-Thu;Alexandre Irrthum;Louis Wehenkel;Pierre Geurts

  • Tree-Based Batch Mode Reinforcement Learning

    Damien Ernst;Pierre Geurts;Louis Wehenkel

  • Understanding variable importances in forests of randomized trees

    Gilles Louppe;Louis Wehenkel;Antonio Sutera;Pierre Geurts

  • Pattern Extraction for Time Series Classification

    Pierre Geurts

  • Random subwindows for robust image classification

    R. Maree;P. Geurts;J. Piater;L. Wehenkel

  • Supervised learning with decision tree-based methods in computational and systems biology

    Pierre Geurts;Alexandre Irrthum;Louis Wehenkel

  • Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge

    Ching-Wei Wang;Cheng-Ta Huang;Meng-Che Hsieh;Chung-Hsing Li

  • dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data.

    Vân Anh Huynh-Thu;Pierre Geurts

  • Collaborative analysis of multi-gigapixel imaging data using Cytomine

    Raphaël Marée;Loïc Rollus;Benjamin Stévens;Renaud Hoyoux

  • MicroRNAs profiling in murine models of acute and chronic asthma: a relationship with mRNAs targets.

    Nancy Garbacki;Emmanuel Di Valentin;Vân Anh Huynh-Thu;Pierre Geurts

  • Proteomic mass spectra classification using decision tree based ensemble methods

    Pierre Geurts;Marianne Fillet;Dominique De Seny;Marie-Alice Meuwis

  • Ensembles on random patches

    Gilles Louppe;Pierre Geurts

  • Discovery of new rheumatoid arthritis biomarkers using the surface‐enhanced laser desorption/ionization time‐of‐flight mass spectrometry ProteinChip approach

    Dominique de Seny;Marianne Fillet;Marie-Alice Meuwis;Pierre Geurts

  • Comparison of Deep Transfer Learning Strategies for Digital Pathology

    Romain Mormont;Pierre Geurts;Raphael Maree

  • Statistical interpretation of machine learning-based feature importance scores for biomarker discovery

    Vân Anh Huynh-Thu;Yvan Saeys;Louis Wehenkel;Pierre Geurts

  • Automated processing of zebrafish imaging data - a survey

    Ralf Mikut;Thomas Dickmeis;Wolfgang Driever;Pierre Geurts

  • Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities

    Alberto Del Angel;Pierre Geurts;Damien Ernst;Mevludin Glavic

  • Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

    Raphaël Liégeois;Erik Ziegler;Christophe Phillips;Pierre Geurts

  • DMFSGD: a decentralized matrix factorization algorithm for network distance prediction

    Yongjun Liao;Wei Du;Pierre Geurts;Guy Leduc

Frequent Co-Authors

Louis Wehenkel
Louis Wehenkel University of Liège
Damien Ernst
Damien Ernst University of Liège
Christophe Phillips
Christophe Phillips University of Liège
Yvan Saeys
Yvan Saeys Ghent University
Marie-Paule Merville
Marie-Paule Merville University of Liège
Marianne Fillet
Marianne Fillet University of Liège
Vincent Bours
Vincent Bours University of Liège
Christine Bastin
Christine Bastin University of Liège
Justus Piater
Justus Piater University of Innsbruck
Marco Wiering
Marco Wiering University of Groningen

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