Publications

Position: Epistemic uncertainty estimation methods are fundamentally incomplete

Sebastián Jiménez, Mira Jürgens, Willem Waegeman

ICML 2026 Position Paper Track 19 citations

Optimal conformal prediction under epistemic uncertainty

Alireza Javanmardi, Soroush H Zargarbashi, Santo MAR Thies, Willem Waegeman, Aleksandar Bojchevski, Eyke Hüllermeier

Uncertainty in Artificial Intelligence 8 citations

Position: agentic AI orchestration should be Bayes-consistent

Theodore Papamarkou, Pierre Alquier, Matthias Bauer, Wray Buntine, Andrew Davison, Gintare Karolina Dziugaite, Maurizio Filippone, Andrew YK Foong, et al.

ICML 2026 Position Paper Track 5 citations

Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?

Gaetan De Waele, Marek Wydmuch, Willem Waegeman

arXiv preprint arXiv:2602.16507 5 citations

Conformal prediction in hierarchical classification with constrained representation complexity

Thomas Mortier, Alireza Javanmardi, Yusuf Sale, Eyke Hüllermeier, Willem Waegeman

AISTATS 2026 5 citations

How negative sampling shapes the performance of transcription factor binding site prediction models

Natan Tourne, Gaetan De Waele, Vanessa Vermeirssen, Willem Waegeman

Bioinformatics 3 citations

Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning

Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier, Vojtěch Franc

arXiv preprint arXiv:2607.14817

Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations

Lucas Boeykens, Devon Dunmire, Jonas-Frederik Jans, Willem Waegeman, Gabriëlle De Lannoy, Ezra Beernaert, Niko EC Verhoest, Hans Lievens

The Cryosphere

Reliable Molecular Retrieval from Mass Spectra Using Conformal Prediction

Morteza Rakhshaninejad, Gaetan De Waele, Mira Jurgens, Willem Waegeman

Journal of Chemical Information and Modeling

Revisiting heterotrophic denitrification modelling: a systematic parameter estimation and uncertainty quantification study

David Fernandes del Pozo, Saba Daneshgar, Willem Waegeman, Ingmar Nopens, Paula Carrera

When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra

Mira Jürgens, Gaetan De Waele, Morteza Rakhshaninejad, Willem Waegeman

arXiv preprint arXiv:2603.10950

A calibration test for evaluating set-based epistemic uncertainty representations

Mira Jürgens, Thomas Mortier, Eyke Hüllermeier, Viktor Bengs, Willem Waegeman

arXiv preprint arXiv:2502.16299 10 citations

Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms

Fauve Vergauwe, Gaetan De Waele, Andrea Sass, Callum Highmore, Niall Hanrahan, Yoshiki Cook, Mads Lichtenberg, Margo Cnockaert, et al.

bioRxiv 9 citations

Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition

Arthur Hoarau, Benjamin Quost, Sébastien Destercke, Willem Waegeman

arXiv preprint arXiv:2501.18268 7 citations

Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics

Lauren Theunissen, Thomas Mortier, Yvan Saeys, Willem Waegeman

bioRxiv 5 citations

Conformal Prediction in Hierarchical Classification

Thomas Mortier, Alireza Javanmardi, Yusuf Sale, Eyke Hüllermeier, Willem Waegeman

arXiv preprint arXiv:2501.19038 5 citations

A systematic assessment of single-cell language model configurations

Gaetan De Waele, Gerben Menschaert, Willem Waegeman

bioRxiv 4 citations

Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and physically-based model simulations

Lucas Boeykens, Devon Dunmire, Jonas-Frederik Jans, Willem Waegeman, Gabriëlle De Lannoy, Ezra Beernaert, Niko EC Verhoest, Hans Lievens

EGUsphere 2 citations

Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli

Friederike Mey, Gaetan De Waele, Wouter Demeester, Chiara Guidi, Dries Duchi, Tomek Diederen, Hanne Kochuyt, Kirsten Van Huffel, et al.

New Biotechnology 1 citations

Valid Prediction Intervals for Weather Forecasting with Conformal Prediction

Thomas Mortier, Cas Decancq, Yusuf Sale, Alireza Javanmardi, Willem Waegeman, Eyke Hüllermeier, Diego G Miralles

EGU25 1 citations

Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction

Morteza Rakhshaninejad, Mira Jurgens, Nicolas Dewolf, Willem Waegeman

Proceedings of Machine Learning Research 266:1–21, 2025 Conformal and Probabilistic Prediction with Applications 1 citations

Snow depth estimation over the Alps from Sentinel-1 polarimetry observations and weather variables using an eXtreme Gradient Booster

Lucas Boeykens, Devon Dunmire, Jonas-Frederik Jans, Willem Waegeman, Ezra Beernaert, Gabrielle De Lannoy, Niko EC Verhoest, Hans Lievens

Living Planet Symposium 2025, Date: 2025/06/23-2025/06/27, Location: Vienna (Austria)

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?

Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

arXiv preprint arXiv:2402.09056 63 citations

A framework for tracing timber following the Ukraine invasion

Thomas Mortier, Jakub Truszkowski, Marigold Norman, Markus Boner, Bogdan Buliga, Caspar Chater, Henry Jennings, Jade Saunders, et al.

Nature Plants 26 citations

Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction

Gaetan De Waele, Gerben Menschaert, Peter Vandamme, Willem Waegeman

bioRxiv 17 citations

Hyperparameter optimization of two-branch neural networks in multi-target prediction

Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman

Applied Soft Computing 14 citations

Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks

Regina Ibragimova, Dimitrios Iliadis, Willem Waegeman

arXiv preprint arXiv:2412.19815 9 citations

Assessment of Uncertainty Quantification in Universal Differential Equations

Nina Schmid, David Fernandes del Pozo, Willem Waegeman, Jan Hasenauer

arXiv preprint arXiv:2406.08853

Author response: An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

Gaetan De Waele, Gerben Menschaert, Willem Waegeman

eLife Sciences Publications, Ltd

Valid prediction intervals for regression problems

Nicolas Dewolf, Bernard De Baets, Willem Waegeman

Artificial Intelligence Review 107 citations

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification

Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

arXiv preprint arXiv:2301.12736 69 citations

Out-of-sample R2: estimation and inference

Stijn Hawinkel, Willem Waegeman, Steven Maere

The American Statistician 44 citations

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

Gaetan De Waele, Willem Waegeman, Gerben Menschaert

bioRxiv 19 citations

A Comparison of Embedding Aggregation Strategies in Drug-Target Interaction Prediction

Dimitrios Iliadis, Bernard De Baets, Tapio Pahikkala, Willem Waegeman

bioRxiv 16 citations

On the Calibration of Probabilistic Classifier Sets

Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman

International Conference on Artificial Intelligence and Statistics 16 citations

Conditional validity of heteroskedastic conformal regression

Nicolas Dewolf, Bernard De Baets, Willem Waegeman

arXiv preprint arXiv:2309.08313 13 citations

Uncertainty-aware single-cell annotation with a hierarchical reject option

Lauren Theunissen, Thomas Mortier, Yvan Saeys, Willem Waegeman

bioRxiv 8 citations

DeepMTP: A Python-based deep learning framework for multi-target prediction

Dimitrios Iliadis, Bernard De Baets, Willem Waegeman

SoftwareX 8 citations

Heteroskedastic conformal regression

Nicolas Dewolf, Bernard De Baets, Willem Waegeman

arXiv preprint arXiv:2309.08313

Pitfalls of epistemic uncertainty quantification through loss minimisation

Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

Advances in Neural Information Processing Systems 118 citations

Novel Transformer Networks for Improved Sequence Labeling in genomics

Jim Clauwaert, Willem Waegeman

IEEE/ACM Transactions on Computational Biology and Bioinformatics 65 citations

CpG Transformer for imputation of single-cell methylomes

Gaetan De Waele, Jim Clauwaert, Gerben Menschaert, Willem Waegeman

Bioinformatics 51 citations

Heterogeneity hampers the identification of general pressure injury risk factors in intensive care populations: a predictive modelling analysis

Mieke Deschepper, Sonia O Labeau, Willem Waegeman, Stijn I Blot, DecubICUs study Team

Intensive and Critical Care Nursing 29 citations

Machine-learning-based prediction of disability progression in multiple sclerosis: an observational, international, multi-center study

Edward De Brouwer, Thijs Becker, Lorin Werthen-Brabants, Pieter Dewulf, Dimitrios Iliadis, Cathérine Dekeyser, Guy Laureys, Bart Van Wijmeersch, et al.

MedRXiv 29 citations

Multi-target prediction for dummies using two-branch neural networks

Dimitrios Iliadis, Bernard De Baets, Willem Waegeman

Machine Learning 21 citations

Seasonal prediction of Horn of Africa long rains using machine learning: The pitfalls of preselecting correlated predictors

Victoria MH Deman, Akash Koppa, Willem Waegeman, David A MacLeod, Michael Bliss Singer, Diego G Miralles

Frontiers in Water 16 citations

Set-valued prediction in hierarchical classification with constrained representation complexity

Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński, Willem Waegeman

International Conference on Uncertainty in Artificial Intelligence 9 citations

Hyperparameter optimization in deep multi-target prediction

Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman

arXiv preprint arXiv:2211.04362

ProD: A Tool for Predictive Design of Tailored Promoters in Escherichia coli

Friederike Mey, Jim Clauwaert, Maarten Van Brempt, Michiel Stock, Jo Maertens, Willem Waegeman, Marjan De Mey

Prokaryotic Gene Regulation: Methods and Protocols, 51-59

Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods

Eyke Hüllermeier, Willem Waegeman

Machine Learning 3517 citations

Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: a large-scale benchmarking study

Thomas Mortier, Anneleen D Wieme, Peter Vandamme, Willem Waegeman

Computational and Structural Biotechnology Journal 100 citations

Explainability in transformer models for functional genomics

Jim Clauwaert, Gerben Menschaert, Willem Waegeman

Briefings in bioinformatics 90 citations

Efficient set-valued prediction in multi-class classification

Thomas Mortier, Marek Wydmuch, Krzysztof Dembczyński, Eyke Hüllermeier, Willem Waegeman

Data Mining and Knowledge Discovery 70 citations

Cytometric fingerprints of gut microbiota predict Crohn’s disease state

Peter Rubbens, Ruben Props, Frederiek-Maarten Kerckhof, Nico Boon, Willem Waegeman

The ISME Journal 42 citations

PhenoGMM: Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure

Peter Rubbens, Ruben Props, Frederiek-Maarten Kerckhof, Nico Boon, Willem Waegeman

MSphere 41 citations

Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting

Jasmine Heyse, Florian Schattenberg, Peter Rubbens, Susann Müller, Willem Waegeman, Nico Boon, Ruben Props

Msystems 27 citations

 Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection

Laure Van den Bulcke, Annelies De Backer, Bart Ampe, Sara Maes, Jan Wittoeck, Willem Waegeman, Kris Hostens, Sofie Derycke

Metabarcoding and Metagenomics 24 citations

Pressure injury prediction models for critically-ill patients should consider both the case-mix and local factors

Mieke Deschepper, Sonia O Labeau, Willem Waegeman, Stijn I Blot, DecubICUs study Team, European Society of Intensive Care Medicine

Intensive & critical care nursing 23 citations

Improving the performance of machine learning models for biotechnology: The quest for deus ex machina

Friederike Mey, Jim Clauwaert, Kirsten Van Huffel, Willem Waegeman, Marjan De Mey

Biotechnology advances 53, 107858 23 citations

High-resolution surveying with small-loop frequency domain electromagnetic systems: Efficient survey design and adaptive processing

Daan Hanssens, Willem Waegeman, Ynse Declercq, Hans Dierckx, Henri Verschelde, Philippe De Smedt

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 9 (1), 282-+ 15 citations

Ambient temperature and relative humidity–based drift correction in frequency domain electromagnetics using machine learning

Daan Hanssens, Ellen Van De Vijver, Willem Waegeman, Mark E Everett, Ian Moffat, Apostolos Sarris, Philippe De Smedt

Near Surface Geophysics 7 citations

A protocol for automated timber species identification using metabolome profiling (vol 53, pg 953, 2019)

V Deklerck, T Mortier, N Goeders, RB Cody, W Waegeman, E Espinoza, J Van Acker, J Van den Bulcke, et al.

WOOD SCIENCE AND TECHNOLOGY

Predictive design of sigma factor-specific promoters

Maarten Van Brempt, Jim Clauwaert, Friederike Mey, Michiel Stock, Jo Maertens, Willem Waegeman, Marjan De Mey

Nature communications 62 citations

Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods

Christina Papagiannopoulou, René Parchen, Peter Rubbens, Willem Waegeman

Analytical chemistry 55 citations

Discriminating bacterial phenotypes at the population and single‐cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting

Cristina García‐Timermans, Peter Rubbens, Jasmine Heyse, Frederiek‐Maarten Kerckhof, Ruben Props, Andre G Skirtach, Willem Waegeman, Nico Boon

Cytometry Part A 38 citations

Algebraic shortcuts for leave-one-out cross-validation in supervised network inference

Michiel Stock, Tapio Pahikkala, Antti Airola, Willem Waegeman, Bernard De Baets

Briefings in Bioinformatics 24 citations

Using structured pathology data to predict hospital-wide mortality at admission

Mieke Deschepper, Willem Waegeman, Dirk Vogelaers, Kristof Eeckloo

Plos one 15 citations

Investigating time series classification techniques for rapid pathogen identification with single-cell MALDI-TOF mass spectrum data

Christina Papagiannopoulou, René Parchen, Willem Waegeman

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III 5 citations

Multi-target prediction: a unifying view on problems and methods

Willem Waegeman, Krzysztof Dembczyński, Eyke Hüllermeier

Data Mining and Knowledge Discovery 139 citations

Multi-target prediction: a unifying view on problems and methods: W. Waegeman et al.

Willem Waegeman, Krzysztof Dembczyński, Eyke Hüllermeier

Data Mining and Knowledge Discovery 139 citations

DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns

Jim Clauwaert, Gerben Menschaert, Willem Waegeman

Nucleic acids research 100 citations

Coculturing bacteria leads to reduced phenotypic heterogeneities

Jasmine Heyse, Benjamin Buysschaert, Ruben Props, Peter Rubbens, Andre G Skirtach, Willem Waegeman, Nico Boon

Applied and Environmental Microbiology 61 citations

A protocol for automated timber species identification using metabolome profiling

Victor Deklerck, Thomas Mortier, Nathalie Goeders, RB Cody, Willem Waegeman, E Espinoza, Joris Van Acker, Jan Van den Bulcke, et al.

Wood Science and Technology 50 citations

A hospital wide predictive model for unplanned readmission using hierarchical ICD data

Mieke Deschepper, Kristof Eeckloo, Dirk Vogelaers, Willem Waegeman

Computer methods and programs in biomedicine 45 citations

Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry

Peter Rubbens, Marian L Schmidt, Ruben Props, Bopaiah A Biddanda, Nico Boon, Willem Waegeman, Vincent J Denef

MSystems 26 citations

Learning single‐cell distances from cytometry data

Bac Nguyen, Peter Rubbens, Frederiek‐Maarten Kerckhof, Nico Boon, Bernard De Baets, Willem Waegeman

Cytometry Part A 7 citations

Predicting microbial biodiversity using flow cytometry and 16S rRNA gene amplicon sequencing

Peter Rubbens, Ruben Props, Frederiek-Maarten Kerckhof, Nico Boon, Willem Waegeman

24th National symposium for Applied Biological Sciences (NSABS 2019)

Effects of chlorhexidine gluconate oral care on hospital mortality: a hospital-wide, observational cohort study

Mieke Deschepper, Willem Waegeman, Kristof Eeckloo, Dirk Vogelaers, Stijn Blot

Intensive care medicine 204 citations

Terrestrial evaporation response to modes of climate variability

Brecht Martens, Willem Waegeman, Wouter A Dorigo, Niko EC Verhoest, Diego G Miralles

NPJ Climate and Atmospheric Science 95 citations

Label-free Raman characterization of bacteria calls for standardized procedures

Cristina García-Timermans, Peter Rubbens, Frederiek-Maarten Kerckhof, Benjamin Buysschaert, Dmitry Khalenkow, Willem Waegeman, Andre G Skirtach, Nico Boon

Journal of microbiological methods 56 citations

A comparative study of pairwise learning methods based on kernel ridge regression

Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman

Neural computation 50 citations

Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data

Ruben Props, Peter Rubbens, Michael Besmer, Benjamin Buysschaert, Jurg Sigrist, Hansueli Weilenmann, Willem Waegeman, Nico Boon, et al.

Water research 50 citations

Deep f-measure maximization in multi-label classification: A comparative study

Stijn Decubber, Thomas Mortier, Krzysztof Dembczyński, Willem Waegeman

Joint European Conference on Machine Learning and Knowledge Discovery in … 26 citations

Global hydro-climatic biomes identified via multitask learning

Christina Papagiannopoulou, Diego G Miralles, Matthias Demuzere, Niko EC Verhoest, Willem Waegeman

Geoscientific Model Development 25 citations

Interpretation and visualisation of data from dairy herds

Kristof Hermans, Geert Opsomer, Willem Waegeman, Sander Moerman, Jenne De Koster, Mieke Van Eetvelde, Bonifacius Van Ranst, Miel Hostens

In Practice 10 citations

Sensitivity of Global Ecosystems to Climate in Observations and Models

Matthias Demuzere, Christina Papagiannopoulou, Jeroen Claessen, Willem Waegeman, Niko Verhoest, Diego G Miralles

AGU Fall Meeting Abstracts

A data-driven search for functional bacterial populations in aquatic systems using flow cytometry, 16S rRNA gene amplicon sequencing and the Randomized Lasso

Peter Rubbens, Marian Schmidt, Ruben Props, Nico Boon, Vincent Denef, Willem Waegeman

ACM KDD 2018 workshop Fragile Earth: Theory Guided Data Science to Enhance Scientific Discovery (FEED@ KDD'18)

Absolute quantification of microbial taxon abundances

Ruben Props, Frederiek-Maarten Kerckhof, Peter Rubbens, Jo De Vrieze, Emma Hernandez Sanabria, Willem Waegeman, Pieter Monsieurs, Frederik Hammes, et al.

The ISME journal 459 citations

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

Christina Papagiannopoulou, Diego G Miralles, Stijn Decubber, Matthias Demuzere, Niko EC Verhoest, Wouter A Dorigo, Willem Waegeman

Geoscientific Model Development 192 citations

Vegetation anomalies caused by antecedent precipitation in most of the world

Christina Papagiannopoulou, DG Miralles, Wouter A Dorigo, NEC Verhoest, Mathieu Depoorter, Willem Waegeman

Environmental Research Letters 191 citations

Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils

Phuong M Nguyen, Amir Haghverdi, Jan De Pue, Yves-Dady Botula, Khoa V Le, Willem Waegeman, Wim M Cornelis

Biosystems engineering 68 citations

Flow cytometric single-cell identification of populations in synthetic bacterial communities

Peter Rubbens, Ruben Props, Nico Boon, Willem Waegeman

PloS one 59 citations

Linear filtering reveals false negatives in species interaction data

Michiel Stock, Timothée Poisot, Willem Waegeman, Bernard De Baets

Scientific reports 35 citations

miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure

Gert Van Peer, Ayla De Paepe, Michiel Stock, Jasper Anckaert, Pieter-Jan Volders, Jo Vandesompele, Bernard De Baets, Willem Waegeman

Nucleic Acids Research 34 citations

Stripping flow cytometry: How many detectors do we need for bacterial identification?

Peter Rubbens, Ruben Props, Cristina Garcia‐Timermans, Nico Boon, Willem Waegeman

Cytometry Part A 26 citations

Novel approaches to assess the quality of fertility data stored in dairy herd management software

Kristof Hermans, Willem Waegeman, Geert Opsomer, Bonifacius Van Ranst, Jenne De Koster, Mieke Van Eetvelde, Miel Hostens

Journal of Dairy Science 19 citations

Analyzing Granger causality in climate data with time series classification methods

Christina Papagiannopoulou, Stijn Decubber, Diego G Miralles, Matthias Demuzere, Niko EC Verhoest, Willem Waegeman

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III 10 15 citations

Potentials and Limitations of Existing Forecasting Models for Alternaria on Potatoes: Challenges for Model Improvement

Sofie Landschoot, Jolien De Reu, Kris Audenaert, Pieter Vanhaverbeke, Geert Haesaert, Bernard De Baets, Willem Waegeman

Potato Research 60, 61-76 15 citations

An exact iterative algorithm for transductive pairwise prediction

Michiel Stock, Bernard De Baets, Willem Waegeman

Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning 1 citations

Investigating the Control of Ocean-Atmospheric Oscillations on Global Terrestrial Evaporation

Brecht Martens, Willem Waegeman, Wouter Dorigo, Niko Verhoest, Diego G Miralles

AGU Fall Meeting Abstracts

A Data-Driven Assessment of the Sensitivity of Global Ecosystems to Climate Anomalies

Diego G Miralles, Christina Papagiannopoulou, Matthias Demuzere, Stijn Decubber, Willem Waegeman, Niko Verhoest, Wouter Dorigo

AGU Fall Meeting Abstracts

Investigating the control of ocean-atmospheric oscillations over global terrestrial evaporation using a simple supervised learning method

Brecht Martens, Diego G Miralles, Wouter A Dorigo, Willem Waegeman, Niko EC Verhoest

2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

Christina Papagiannopoulou, Stijn Decubber, Diego Miralles, Matthias Demuzere, Wouter Dorigo, Niko Verhoest, Willem Waegeman

EGU General Assembly Conference Abstracts

Investigating the control of climatic oscillations over global terrestrial evaporation using a simple supervised learning method

Brecht Martens, Diego Miralles, Willem Waegeman, Wouter Dorigo, Niko Verhoest

EGU General Assembly Conference Abstracts

Sensitivity of Global Ecosystems to Climate Anomalies in Observations and Earth System Models

Matthias Demuzere, Stijn Decubber, Diego Miralles, Christina Papagiannopoulou, Willem Waegeman, Niko Verhoest, Wouter Dorigo

NCAR Technical Notes NCAR/TN536+ PROC

The use of shallow convolutional neural networks in predicting promotor strength in Escherichia coli

Jim Clauwaert, Michiel Stock, Marjan De Mey, Willem Waegeman

Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, Technische Universiteit Eindhoven, 9-10 June 2017

Data-driven recipe completion using machine learning methods

Marlies De Clercq, Michiel Stock, Bernard De Baets, Willem Waegeman

Trends in Food Science & Technology 49, 1-13 53 citations

Consistency of probabilistic classifier trees

Krzysztof Dembczyński, Wojciech Kotłowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II 16 22 citations

Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

Michiel Stock, Krzysztof Dembczyński, Bernard De Baets, Willem Waegeman

Data Mining and Knowledge Discovery 3 citations

Potential for novel glycan measurements in milk as biomarker phenotypes for dairy traits

A Santoro, J Vandepitte, M Hostens, F Carter, E Matthews, W Waegeman, AG Fahey, K Hermans, et al.

Book of abstracts of the 67th Annual Meeting of the European Federation of … 3 citations

Replacing a food allergen in a recipe using data-driven methods

Marlies De Clercq, Floris Ramon, Bernard De Baets, Willem Waegeman

Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium 1 citations

CCI soil moisture for improved understanding of terrestrial ecosystem dynamics

Wouter Dorigo, Matthias Forkel, Irene Teubner, Bernhard Bauer-Marschallinger, Mathieu Depoorter, Diego Miralles, Christina Papagiannopoulou, Willem Waegeman, et al.

Identifying synthetic microbial communities by learning in silico communities using flow cytometry

Peter Rubbens, Willem Waegeman, Ruben Props, Nico Boon

10th International workshop on Machine Learning in Systems Biology (MLSB 2016); held at the 15th European conference on Computational Biology (ECCB 2016)

Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities

Peter Rubbens, Ruben Props, Nico Boon, Willem Waegeman

4th Workshop on Machine Learning in the Life Sciences; organized under the European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2016

Clustering environmental flow cytometry data by searching density peaks

Peter Rubbens, Willem Waegeman, Ruben Props, Nico Boon

25th Annual Belgian-Dutch conference on Machine Learning (BENELEARN)

Prediction of subacute ruminal acidosis based on milk fatty acids: A comparison of linear discriminant and support vector machine approaches for model development

Ellen Colman, Willem Waegeman, Bernard De Baets, Veerle Fievez

Computers and Electronics in Agriculture 38 citations

Spectral analysis of symmetric and anti-symmetric pairwise kernels

Tapio Pahikkala, Markus Viljanen, Antti Airola, Willem Waegeman

arXiv preprint arXiv:1506.05950 7 citations

Global impacts of hydrological and climatic extremes on vegetation (SAT-EX)

Christel Melissa van Eck, Willem Waegeman, Christina Papagiannopoulou, Niko Verhoest, Mathieu Depoorter, Pierre Regnier, Pierre Friedlingstein, A Johannes Dolman, et al.

EGU General Assembly Conference Abstracts

Trends in ESA CCI soil moisture: potential and caveats

Wouter Dorigo, Stefan Schlaffer, Bernhard Bauer-Marschallinger, Clement Albergel, Christina Papagiannopoulou, Willem Waegeman, Mathieu Depoorter, Diego Miralles, et al.

Trends in Means and Extremes of ESA CCI Soil Moisture

Wouter Dorigo, Stefan Schlaffer, Mathieu Depoorter, Diego Miralles, Christina Papagiannopoulou, Niko Verhoest, Willem Waegeman, Wolfgang Wagner

Recipe completion using machine learning techniques

Marlies De Clercq, Michiel Stock, Bernard De Baets, Willem Waegeman

ICML 2015 Workshop on Constructive Machine Learning

A community effort to assess and improve drug sensitivity prediction algorithms

James C Costello, Laura M Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P Menden, Nicholas J Wang, Mukesh Bansal, Muhammad Ammad-Ud-Din, et al.

Nature biotechnology 930 citations

On the bayes-optimality of f-measure maximizers

Willem Waegeman, Krzysztof Dembczyński, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier

Journal of Machine Learning Research 103 citations

A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II 14 29 citations

Predicting spatio-temporal Culicoides imicola distributions in Spain based on environmental habitat characteristics and species dispersal

Jan Peters, Willem Waegeman, E Ducheyne, C Calvete, J Lucientes, NEC Verhoest, Bernard De Baets

Ecological informatics 12 citations

Identification of functionally related enzymes by learning-to-rank methods

Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, et al.

IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 citations

Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models

Shinji Fukuda, Bernard De Baets, Willem Waegeman, Jan Verwaeren, Ans M Mouton

Environmental modelling & software 176 citations

Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization

Krzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski, Willem Waegeman, Eyke Hüllermeier

International conference on machine learning 147 citations

Exploration and prediction of interactions between methanotrophs and heterotrophs

Michiel Stock, Sven Hoefman, Frederiek-Maarten Kerckhof, Nico Boon, Paul De Vos, Bernard De Baets, Kim Heylen, Willem Waegeman

Research in microbiology 99 citations

Influence of maize–wheat rotation systems on Fusarium head blight infection and deoxynivalenol content in wheat under low versus high disease pressure

Sofie Landschoot, Kris Audenaert, Willem Waegeman, Bernard De Baets, Geert Haesaert

Crop Protection 69 citations

Efficient regularized least-squares algorithms for conditional ranking on relational data

Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman

Machine Learning 55 citations

A field-specific web tool for the prediction of Fusarium head blight and deoxynivalenol content in Belgium

Sofie Landschoot, Willem Waegeman, Kris Audenaert, P Van Damme, Judith Vandepitte, Bernard De Baets, Geert Haesaert

Computers and electronics in agriculture 48 citations

Ordinal regression models for predicting deoxynivalenol in winter wheat

Sofie Landschoot, Willem Waegeman, Kris Audenaert, Geert Haesaert, Bernard De Baets

Plant pathology 27 citations

Combined exposure to cyanobacteria and carbaryl results in antagonistic effects on the reproduction of Daphnia pulex

Jana Asselman, Joris Meys, Willem Waegeman, Bernard De Baets, Karel AC De Schamphelaere

Environmental toxicology and chemistry 24 citations

Plant disease prediction using data mining and machine learning: a case study on Fusarium head blight and deoxynivalenol content in winter wheat

Sofie Landschoot, Kris Audenaert, Willem Waegeman, Bernard De Baets, Geert Haesaert

12th European Fusarium Seminar

Erratum to “The logistic curve as a tool to describe the daily ruminal pH pattern and its link with milk fatty acids”(J. Dairy Sci. 95: 5845–5865)

E Colman, BM Tas, W Waegeman, B De Baets, V Fievez

Journal of Dairy Science

Does risk assessment of harmful algal blooms need to consider combined and interactive effects with pesticides (and vice versa)?

Jana Asselman, Michael Pfrender, Jacqueline Lopez, Joseph Shaw, Willem Waegeman, Bernard De Baets, Karel De Schamphelaere

Setac North America 34th Annual Meeting

List of Publications: Willem Waegeman

W Waegeman

Systematic and Applied Microbiology

A kernel-based model to predict interaction between methanotrophic and heterotrophic bacteria

M Stock, S Hoefman, FM Kerckhof, N Boon, P De Vos, K Heylen, B De Baets, W Waegeman

Communications in agricultural and applied biological sciences

On label dependence and loss minimization in multi-label classification

Krzysztof Dembczyński, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

Machine Learning 557 citations

An Analysis of Chaining in Multi-Label Classification.

Krzysztof Dembczynski, Willem Waegeman, Eyke Hüllermeier

ECAI 91 citations

An empirical analysis of explanatory variables affecting Fusarium head blight infection and deoxynivalenol content in wheat

Sofie Landschoot, Willem Waegeman, Kris Audenaert, Jan Vandepitte, JM Baetens, Bernard De Baets, Geert Haesaert

Journal of Plant Pathology 65 citations

Label ranking with partial abstention based on thresholded probabilistic models

Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker

Advances in neural information processing systems 63 citations

A kernel-based framework for learning graded relations from data

Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets

IEEE Transactions on Fuzzy Systems 42 citations

Learning partial ordinal class memberships with kernel-based proportional odds models

Jan Verwaeren, Willem Waegeman, Bernard De Baets

Computational Statistics & Data Analysis 41 citations

The logistic curve as a tool to describe the daily ruminal pH pattern and its link with milk fatty acids

Ellen Colman, BM Tas, Willem Waegeman, Bernard De Baets, Veerle Fievez

Journal of dairy science 39 citations

Toward a reliable evaluation of forecasting systems for plant diseases: a case study using Fusarium head blight of wheat

Sofie Landschoot, Willem Waegeman, Kris Audenaert, Judith Vandepitte, Geert Haesaert, Bernard De Baets

Plant Disease 37 citations

F-measure maximization in topical classification

Weiwei Cheng, Krzysztof Dembczyński, Eyke Hüllermeier, Adrian Jaroszewicz, Willem Waegeman

Rough Sets and Current Trends in Computing: 8th International Conference, RSCTC 2012, Chengdu, China, August 17-20, 2012. Proceedings 8 13 citations

Statistical relational learning for prognostics

Jonas Vlasselaer, Wannes Meert, Bernard De Baets, Bernard Manderick, Michael Rademaker, Willem Waegeman

Proceedings of the 21st Belgian-Dutch Conference on Machine Learning 8 citations

Learning monadic and dyadic relations: three case studies in systems biology

Michiel Stock, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets, Willem Waegeman

ECML/PKDD 2012 workshop on Learning and Discovery in Symbolic Systems Biology (LDSSB'12) 2 citations

A field-specific webtool for the prediction of Fusarium head blight and DON content in Belgium

Sofie Landschoot, Willem Waegeman, Kris Audenaert, J Vandepitte, Patrick Van Damme, Bernard De Baets, Geert Haesaert

64th International symposium on Crop Protection 1 citations

Learning valued relations from data

Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets

Eurofuse 2011: Workshop on Fuzzy Methods for Knowledge-Based Systems 1 citations

BeneLearn 2012: proceedings of the 21st Belgian-Dutch conference on machine learning

Bernard De Baets, Bernard Manderick, Michaël Rademaker, Willem Waegeman

21st Belgian-Dutch conference on Machine Learning (BeneLearn 2012)

Applying morphological changes during the evolution of quadruped robots results in robust gaits

B De Baets, B Manderick, Ml Rademaker, W Waegeman

Proceedings of the 21st Belgian-Dutch Conference on Machine Learning

A machine learning approach to predict Fusarium head blight

Sofie Landschoot, Willem Waegeman, Kris Audenaert, Judith Vandepitte, Patrick Van Damme, Bernard De Baets, Geert Haesaert

21st Annual Belgian-Dutch conference on Machine Learning (BeNeLearn & PMLS 2012)

Conditional ranking algorithms for efficient object retrieval and object querying on relational data

Willem Waegeman, Michiel Stock, Bernard De Baets, Tapio Pahikkala, Antti Airola, Tapio Salakoski

12th Dutch-Belgian Information Retrieval Workshop (DIR 2012)

Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons

Shinji Fukuda, Willem Waegeman, Ans Mouton, Bernard De Baets

Eurofuse 2011: Workshop on Fuzzy Methods for Knowledge-Based Systems

Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning

Katrien De Bruyne, Bram Slabbinck, Willem Waegeman, Paul Vauterin, Bernard De Baets, Peter Vandamme

Systematic and applied microbiology 300 citations

An experimental comparison of cross-validation techniques for estimating the area under the ROC curve

Antti Airola, Tapio Pahikkala, Willem Waegeman, Bernard De Baets, Tapio Salakoski

Computational Statistics & Data Analysis 240 citations

An exact algorithm for F-measure maximization

Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

Advances in neural information processing systems 169 citations

Connection between primary Fusarium inoculum on gramineous weeds, crop residues and soil samples and the final population on wheat ears in Flanders, Belgium

Sofie Landschoot, Kris Audenaert, Willem Waegeman, Bart Pycke, Boris Bekaert, Bernard De Baets, Geert Haesaert

Crop Protection 83 citations

Effect of model formulation on the optimization of a genetic Takagi–Sugeno fuzzy system for fish habitat suitability evaluation

Shinji Fukuda, Bernard De Baets, Ans M Mouton, Willem Waegeman, Jun Nakajima, Takahiko Mukai, Kazuaki Hiramatsu, Norio Onikura

Ecological Modelling 69 citations

Impact of fungicide timing on the composition of the Fusarium head blight disease complex and the presence of deoxynivalenol (DON) in wheat

Kris Audenaert, Sofie Landschoot, Adriaan Vanheule, Willem Waegeman, Bernard De Baets, Geert Haesaert

Fungicides-Beneficial and Harmful Aspects 36 citations

Supervised learning algorithms for multi-class classification problems with partial class memberships

Willem Waegeman, Jan Verwaeren, Bram Slabbinck, Bernard De Baets

Fuzzy Sets and Systems 29 citations

On the ERA ranking representability of multi-class classifiers

W Waegeman, B De Baets

Artificial Intelligence 27 citations

Joint mode estimation in multi-label classification by chaining

Krzysztof Dembczyński, Willem Waegeman, Eyke Hüllermeier

CoLISD 2011 (Collective Learning and Inference on Structured Data 2011) 7 citations

ERA ranking representability: The missing link between ordinal regression and multi-class classification

Willem Waegeman, Bernard De Baets

2011 11th International Conference on Intelligent Systems Design and Applications

An analysis of explanatory variables affecting Fusarium infection and DON content in wheat

Sofie Landschoot, Willem Waegeman, Kris Audenaert, J Vandepitte, J Baeten, Bernard De Baets, Geert Haesaert

63rd International symposium on Crop Protection

A kernel framework for learning relations from paired-comparison data

Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets

4th International conference of the ERCIM Working Group on Computing and Statistics (ERCIM 2011)

Modelling spawning habitat for European grayling (Thymallus thymallus L.) using a support vector machine and a genetic Takagi-Sugeno fuzzy model

Shinji Fukuda, Willem Waegeman, Ans Mouton, Bernard De Baets

7th European conference on Ecological Modelling (ECEM 2011)

Incorporating prior knowledge in multiple output regression with application to chemometrics

Jan Verwaeren, Willem Waegeman, Bernard De Baets

Benelearn 2011

Regret analysis for performance metrics in multi-label classification: the case of hamming and subset zero-one loss

Krzysztof Dembczyński, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I 21 88 citations

Learning intransitive reciprocal relations with kernel methods

Tapio Pahikkala, Willem Waegeman, Evgeni Tsivtsivadze, Tapio Salakoski, Bernard De Baets

European Journal of Operational Research 52 citations

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification

Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Paul De Vos, Bernard De Baets

BMC bioinformatics 35 citations

From circular ordinal regression to multilabel classification

Dieter Devlaminck, Willem Waegeman, Bruno Bauwens, Bart Wyns, Patrick Santens, Georges Otte

Proceedings of the 2010 Workshop on Preference Learning (European Conference on Machine Learning, ECML) 24 citations

A survey on ROC-based ordinal regression

Willem Waegeman, Bernard De Baets

Preference learning, 127-154 17 citations

A transitivity analysis of bipartite rankings in pairwise multi-class classification

Willem Waegeman, Bernard De Baets

Information Sciences 14 citations

Directional predictions for 4-class BCI data

Dieter Devlaminck, Willem Waegeman, Bruno Bauwens, Bart Wyns, Georges Otte, Luc Boullart, Patrick Santens

18th European Symposium on Artificial Neural Networks: Computational Intelligence and Machine Learning (ESANN-2010) 4 citations

On the role of cost-sensitive learning in multi-class brain-computer interfaces

Dieter Devlaminck, Willem Waegeman, Bart Wyns, Georges Otte, Patrick Santens

Walter de Gruyter 55 (3), 163-172 2 citations

A genetic Takagi-Sugeno fuzzy system for fish habitat preference modelling

Shinji Fukuda, Norio Onikura, Ans M Mouton, Bernard De Baets, Willem Waegeman, Jun Nakajima, Takahiko Mukai

2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC) 1 citations

Decomposition methods for multi-label learning of compositional data

Jan Verwaeren, Willem Waegeman, Bernard De Baets

Working Notes

An empirical analysis of explanatory variables affecting Fusarium and deoxynivalenol in wheat for feed production.

Sofie Landschoot, Kris Audenaert, Willem Waegeman, Jan Vandepitte, Geert Haesaert, B de Baets

2nd Workshop Feed-to-Food FP7 REGPOT-3. XIV International Symposium feed technology, Proceedings. Novi Sad, Serbia, 19-21 October

Composition of primary Fusarium head blight (FHB) inoculums and the final population on wheat ears

Sofie Landschoot, Kris Audenaert, Willem Waegeman, Bernard De Baets, Bart Pycke, Boris Bekaert

62nd International symposium on Crop Protection

Ensemble methods for multi-label learning of compositional data

Jan Verwaeren, Willem Waegeman, Bernard De Baets

2nd International workshop on Learning from Multi-label Data (MLD'10)

Learning partial ordinal class memberships in a proportional odds setting

Jan Verwaeren, Willem Waegeman, Bernard De Baets

19th Conference on Machine Learning in the Benelux (Benelearn 2010)

Conditional ranking on relational data

Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets

24th European conference on Operations Research (EURO XXIV)

A machine learning approach to predict Fusarium head blight in winter wheat

Sofie Landschoot, Kris Audenaert, Willem Waegeman, Bernard De Baets, Geert Haesaert

11th European Fusarium seminar: Fusarium: mycotoxins, taxonomy, pathogenicity and host resistance IHAR

Relationship between primary Fusarium inoculum and the final population on wheat ears

Sofie Landschoot, Kris Audenaert, Willem Waegeman, Bernard De Baets, Geert Haesaert

11th European Fusarium Seminar: Fusarium: mycotoxins, taxonomy, pathogenicity and host resistance IHAR

A comparison of AUC estimators in small-sample studies

Antti Airola, Tapio Pahikkala, Willem Waegeman, Bernard De Baets, Tapio Salakoski

Machine learning in systems biology 111 citations

An ensemble of weighted support vector machines for ordinal regression

Willem Waegeman, Luc Boullart

International Journal of Computer Systems Science and Engineering 83 citations

Kernel-based learning methods for preference aggregation

Willem Waegeman, Bernard De Baets, Luc Boullart

4OR 29 citations

Learning to rank: a ROC-based graph-theoretic approach

Willem Waegeman

4OR 3 citations

On the use of ordinal regression and cost-sensitive classification in 3-class brain-computer interfaces

Dieter Devlaminck, Willem Waegeman, Bart Wyns, Georges Otte, Patrick Santes

NEUROMATH Workshop 2009 (FP7-COST): Advanced methods for the estimation of human brain activity and connectivity

Machine learning algorithms for coupling FAME and 16S rRNA data in bacterial species identification

Dries Van Audenhove, Willem Waegeman, Bram Slabbinck, Peter Dawyndt, Bernard De Baets

5th Benelux Bioinformatics Conference (BBC 2009)

Learning Intransitive Reciprocal Relations with Regularized Least-Squares

Tapio Pahikkala, Willem Waegeman, Evgeni Tsivtsivadze, B De Baets, T Salakoski

The 18th Annual Belgian-Dutch Conference on Machine Learning

Machine learning algorithms for disease prediction in winter wheat

Dorien Colman, Willem Waegeman, Kris Audenaert, Bernard De Baets, Geert Haesaert

18th Annual Belgian-Dutch conference on Machine Learning (Benelearn 09)

Modelling partial class memberships in multi-class classification: a probabilistic approach

Willem Waegeman, Bernard De Baets

EUROFUSE workshop 09: Preference handling and decision analysis

Learning ordinal partial class memberships with kernel-based proportional odds models

Willem Waegeman, Jan Verwaeren, Bernard De Baets

3rd International conference on Computational and Financial Econometrics (CFE'09)

A transitivity analysis of bipartite rankings in a pairwise multi-class setting

Willem Waegeman, Bernard De Baets

23rd Belgian conference on Operations Research (ORBEL'09)

Ordinal Regression for a 3-class Brain-Computer Interface

Dieter Devlaminck, Willem Waegeman, Bart Wyns, Luc Boullart, Georges Otte, PC Dr Guislain, Patrick Santens

The 18th Annual Belgian-Dutch Conference on Machine Learning

MMN: emotional dimension of distractor task in a simple duration mismatch paradigm

Georges Otte, G De-Bruecker, Willem Waegeman, Pieter-Jan Geerts, Bart Wyns

Conference Abstract: MMN 09 Fifth Conference on Mismatch Negativity (MMN) and its Clinical and Scientific Applications. doi: 10.3389/conf. neuro

ROC analysis in ordinal regression learning

Willem Waegeman, Bernard De Baets, Luc Boullart

Pattern Recognition Letters 180 citations

On the scalability of ordered multi-class ROC analysis

Willem Waegeman, Bernard De Baets, Luc Boullart

Computational Statistics & Data Analysis 48 citations

Classifying carpets based on laser scanner data

Willem Waegeman, Johannes Cottyn, Bart Wyns, Luc Boullart, Bernard De Baets, Lieva Van Langenhove, Jan Detand

Engineering Applications of Artificial Intelligence 24 citations

Learning layered ranking functions with structured support vector machines

Willem Waegeman, Bernard De Baets, Luc Boullart

Neural Networks 23 citations

Integrating expert knowledge into kernel-based preference models

Willem Waegeman, Bernard De Baets, Luc Boullart

Proceedings of the ECML Workshop on Preference Learning, Antwerp, Belgium 3 citations

When can we simplify a one-versus-one multi-class classifier to a single ranking

Willem Waegeman, Bernard De Baets, Luc Boullart

Proceedings of the Annual Belgian-Dutch Machine Learning Conference

Polymorphisms in the ficolin 1 gene (FCN1) are associated with susceptibility to the development of rheumatoid arthritis

BERT Vander Cruyssen, Lieve Nuytinck, Luc Boullart, Dirk Elewaut, Willem Waegeman, Mira Van Thielen, E De Meester, K Lebeer, et al.

Rheumatology 74 citations

ROC-based ordinal regression learning: an overview

Willem Waegeman, Bernard De Baets, Luc Boullart

2007 International conference on Ordinal and Symbolic Data Analysis (OSDA 2007)

Kernel-based aggregation of fuzzy and probabilistic preference relations

Willem Waegeman, Bernard De Baets, Luc Boullart

International doctoral school on Algorithmic Decision Theory

Ranking by learning layered graphs

Willem Waegeman, Bernard De Baets, Luc Boullart

17th Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn 2007)

Machine learning algorithms for carpets classification

Rares Damian, Robain De Keyser, Luc Boullart, Willem Waegeman, Lieva Van Langenhove, Corneliu Lazar

9th International Symposium on Automatic Control and Computer Science

A comparison of different ROC measures for ordinal regression

Willem Waegeman, Bernard De Baets, Luc Boullart

Proceedings of the CML 2006 workshop on ROC Analysis in Machine Learning 23 citations

The volume under the ROC surface as evaluation criterion for ordinal regression models

Willem Waegeman, Bernard De Baets, Luc Boullart

2006 International workshop on Current Challenges in Kernel Methods (CCKM 2006)

Ordinal classification with support vector machines for automatic assessment of carpet wear

Willem Waegeman, Bernard De Baets, Luc Boullart

5e FirW PhD Symposium

3.15 Is zero-shot learning possible without side information?

Willem Waegeman

Extreme Classification

Set-valued prediction in hierarchical classification

Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski34, Willem Waegeman

Global hydro-climatic biomes via mulit-task learning Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E.

C Verhoest, Willem Waegeman

Discovering relationships in climate-vegetation dynamics

Christina Papagiannopoulou, Diego Miralles, Mathieu Depoorter, Niko EC Verhoest, Wouter Dorigo, Willem Waegeman

A non-linear Granger causality approach for understanding climate-vegetation dynamics

Christina Papagiannopoulou, Stijn Decubber, Willem Waegeman, Matthias Demuzere, Niko EC Verhoest, Diego G Miralles

Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, Technische Universiteit Eindhoven, 9-10 June 2017

A machine learning approach to disease prediction in winter wheat

Dorien Colman, Willem Waegeman, Kris Audenaert, Bernard De Baets, Geert Haesaert

The 18th Annual Belgian-Dutch Conference on Machine Learning

Enzyme Annotation using Conditional Ranking Algorithms

Michiel Stock, Bernard De Baets, Willem Waegeman, Thomas Fober, Eyke Hüllermeier, Tapio Pahikkala, Antti Airola

BENELEARN 2014

Learning a Layered Graph with a Maximal Number of Distinct Paths Connecting Source and Sink

Willem Waegeman, Bernard De Baets, Luc Boullart

Learning Relations: Pitfalls and Applications

Michiel Stock, Willem Waegeman, Bernard De Baets

Leren rangschikken: een ROC-gebaseerde grafentheoretische benadering Learning to Rank: a ROC-Based Graph-Theoretic Approach

Willem Waegeman

Leren rangschikken: een ROC-gebaseerde grafentheoretische benadering

Willem Waegeman

Transformers for MALDI-TOF mass spectra

Gaetan De Waele, Anneleen D Wieme, Gerben Menschaert, Peter Vandamme, Willem Waegeman

Correction to: A protocol for automated timber species identification using metabolome profiling, 2019, Vol. 53

V Deklerck, T Mortier, N Goeders, RB Cody, W Waegeman, E Espinoza, J Van Acker, J Van den Bulcke, et al.

Springer, 0

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Thomas Mortier, Jakub Truszkowski, Marigold Norman, Markus Boner, Bogdan Buliga, Caspar Chater, Henry Jennings, Jade Saunders, et al.

Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas

Fauve Vergauwe, Gaetan De Waele, Andrea Sass, Callum Highmore, Niall Hanrahan, Yoshiki Cook, Mads Lichtenberg, Margo Cnockaert, et al.

PROGRESSION OF AUTOANTIBODIES ANTI-GAD AND ANTI-IA2 IN TYPE 1A DIABETICS AGED OF 5 TO 21 YEARS IN CÔTE D’IVOIRE

Aïssé Florence Judith Trébissou, Chiayé Claire Antoinette Yapo-Crezoit, Pascal Sibailly, Mamadou Sanogo, Amos Ankotché, Dinard Kouassi, Marie Thérèse Kouassi, Hatem Masmoudi, et al.

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