W. Waegeman

Niko E. C. Verhoest, Willem Waegeman, Christina Papagiannopoulou, 2017, 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp).

ROC analysis in ordinal regression learning

Bernard De Baets, Willem Waegeman, Luc Boullart, 2008, Pattern Recognit. Lett..

Investigating Time Series Classification Techniques for Rapid Pathogen Identification with Single-Cell MALDI-TOF Mass Spectrum Data

Willem Waegeman, Christina Papagiannopoulou, René Parchen, 2019, BNAIC/BENELEARN.

Analyzing Granger Causality in Climate Data with Time Series Classification Methods

Niko E. C. Verhoest, Willem Waegeman, Christina Papagiannopoulou, 2017, ECML/PKDD.

Clustering environmental flow cytometry data by searching density peaks

Willem Waegeman, Nico Boon, Peter Rubbens, 2016 .

Valid prediction intervals for regression problems

Bernard De Baets, Willem Waegeman, Nicolas Dewolf, 2021, Artificial Intelligence Review.

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

W Waegeman, M Deschepper, K Eeckloo, 2019, Comput. Methods Programs Biomed..

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

W. Waegeman, S. Derycke, B. Ampe, 2021, Metabarcoding and Metagenomics.

An ensemble of Weighted Support Vector Machines for Ordinal Regression

Willem Waegeman, Luc Boullart, W. Waegeman, 2007 .

Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction pdf

Willem Waegeman, Eyke Hüllermeier, E. Hüllermeier, 2019, ArXiv.

Kernel-based learning methods for preference aggregation

Bernard De Baets, Willem Waegeman, Luc Boullart, 2009, 4OR.

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

W. Waegeman, D. Vogelaers, M. Deschepper, 2020, PloS one.

Predictive design of sigma factor-specific promoters

Michiel Stock, Willem Waegeman, Jim Clauwaert, 2020, Nature Communications.

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

M. De Mey, W. Waegeman, Jim Clauwaert, 2021, Biotechnology advances.

A protocol for automated timber species identification using metabolome profiling

W. Waegeman, R. Cody, H. Beeckman, 2019, Wood Science and Technology.

Fast pathogen identification using single-cell MALDI-ATOF mass spectrometry data and deep learning methods.

W. Waegeman, P. Rubbens, R. Parchen, 2020, Analytical chemistry.

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

Willem Waegeman, Dieter Devlaminck, Bart Wyns, 2010, Biomedizinische Technik. Biomedical engineering.

Terrestrial evaporation response to modes of climate variability

N. Verhoest, W. Dorigo, W. Waegeman, 2018, npj Climate and Atmospheric Science.

Integrating Expert Knowledge into Kernel-based Preference Models

Bernard De Baets, Willem Waegeman, Luc Boullart, 2008 .

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2018, Neural Computation.

A kernel framework for learning relations from paired-comparison data

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2011 .

Learning Single‐Cell Distances from Cytometry Data

Willem Waegeman, Bernard De Baets, Frederiek-Maarten Kerckhof, 2019, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

Out-of-sample R2: estimation and inference

Steven Maere, W. Waegeman, Stijn Hawinkel, 2023, The American Statistician.

Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation

E. Hüllermeier, W. Waegeman, Viktor Bengs, 2022, NeurIPS.

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification

E. Hüllermeier, W. Waegeman, Viktor Bengs, 2023, ArXiv.

Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method pdf

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2016, ArXiv.

A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2014, ECML/PKDD.

On the scalability of ordered multi-class ROC analysis

Bernard De Baets, Willem Waegeman, Luc Boullart, 2008, Comput. Stat. Data Anal..

Consistency of Probabilistic Classifier Trees

Eyke Hüllermeier, Wojciech Kotlowski, Róbert Busa-Fekete, 2016, ECML/PKDD.

Cytometric fingerprints of gut microbiota predict Crohn’s disease state

W. Waegeman, N. Boon, R. Props, 2020, The ISME Journal.

Efficient set-valued prediction in multi-class classification

Eyke Hüllermeier, Thomas Mortier, Marek Wydmuch, 2019, Data Mining and Knowledge Discovery.

Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization

Eyke Hüllermeier, Wojciech Kotlowski, Willem Waegeman, 2013, ICML.

An Exact Algorithm for F-Measure Maximization

Eyke Hüllermeier, Willem Waegeman, Weiwei Cheng, 2011, NIPS.

Automated problem setting selection in multi-target prediction with AutoMTP pdf

Bernard De Baets, Willem Waegeman, Dimitrios Iliadis, 2021, ArXiv.

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

Bernard De Baets, Willem Waegeman, Jan Verwaeren, 2012, Comput. Stat. Data Anal..

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

B. Baets, Michiel Stock, K. Dembczynski, 2016, Data Mining and Knowledge Discovery.

Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification pdf

Eyke Hüllermeier, Willem Waegeman, Thomas Mortier, 2019, ArXiv.

A Survey on ROC-based Ordinal Regression

Bernard De Baets, Willem Waegeman, B. Baets, 2010, Preference Learning.

Machine learning algorithms for carpets classification

Robain De Keyser, Corneliu Lazar, Willem Waegeman, 2007 .

An Analysis of Chaining in Multi-Label Classification

Eyke Hüllermeier, Willem Waegeman, Krzysztof Dembczynski, 2012, ECAI.

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

W. Waegeman, 2009, 4OR.

Learning layered ranking functions with structured support vector machines

Bernard De Baets, Willem Waegeman, Luc Boullart, 2008, Neural Networks.

Classifying carpets based on laser scanner data

Bernard De Baets, Jan Detand, Willem Waegeman, 2008, Eng. Appl. Artif. Intell..

A graph-theoretic approach for reducing one-versus-one multi-class classification to ranking

W. Waegeman, 2008 .

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

Bernard De Baets, Willem Waegeman, Luc Boullart, 2007 .

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

Bernard De Baets, Michiel Stock, Willem Waegeman, 2016, Data Mining and Knowledge Discovery.

Exploration and prediction of interactions between methanotrophs and heterotrophs.

B. De Baets, P. de Vos, Michiel Stock, 2013, Research in microbiology.

Conditional Ranking on Relational Data

Tapio Salakoski, Bernard De Baets, Tapio Pahikkala, 2010, ECML/PKDD.

A comparison of embedding aggregation strategies in drug–target interaction prediction

T. Pahikkala, W. Waegeman, Bernard De Baets, 2023, bioRxiv.

Bayesian Learning for Binary Neural Networks

E. Hüllermeier, W. Waegeman, V. Y. A. Shekhovtsov, 2022 .

High-Resolution Surveying With Small-Loop Frequency Domain Electromagnetic Systems: Efficient Survey Design and Adaptive Processing

Philippe De Smedt, Willem Waegeman, Hans Dierckx, 2021, IEEE Geoscience and Remote Sensing Magazine.

Valid prediction intervals for regression problems

B. Baets, W. Waegeman, Nicolas Dewolf, 2021, Artificial Intelligence Review.

Global hydro-climatic biomes identified via multitask learning

N. Verhoest, W. Waegeman, M. Demuzere, 2018, Geoscientific Model Development.

On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation

W. Waegeman, Viktor Bengs, Eyke Hüllermeier, 2022, ArXiv.

Modelling Fish Habitat Preference with a Genetic Algorithm-Optimized Takagi-Sugeno Model Based on Pairwise Comparisons

Bernard De Baets, Willem Waegeman, Shinji Fukuda, 2011, Eurofuse.

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

Bernard De Baets, Tapio Pahikkala, Antti Airola, 2012, Machine Learning.

Incorporating prior knowledge in multiple output regression with application to chemometrics

Bernard De Baets, Willem Waegeman, Jan Verwaeren, 2011 .

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

M. Everett, A. Sarris, W. Waegeman, 2021 .