Willem Waegeman

According to our database, Willem Waegeman authored at least 74 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

A comparison of embedding aggregation strategies in drug-target interaction prediction.

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

BMC Bioinform., December, 2024

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?

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

CoRR, 2024

2023

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

Dimitrios Iliadis, Bernard De Baets, Willem Waegeman

SoftwareX, July, 2023

Heteroskedastic conformal regression.

Nicolas Dewolf, Bernard De Baets, Willem Waegeman

CoRR, 2023

Valid prediction intervals for regression problems.

Nicolas Dewolf, Bernard De Baets, Willem Waegeman

Artif. Intell. Rev., 2023

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification.

Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

Proceedings of the International Conference on Machine Learning, 2023

On the Calibration of Probabilistic Classifier Sets.

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Novel Transformer Networks for Improved Sequence Labeling in genomics.

Jim Clauwaert, Willem Waegeman

IEEE ACM Trans. Comput. Biol. Bioinform., 2022

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

Dimitrios Iliadis, Bernard De Baets, Willem Waegeman

Mach. Learn., 2022

Hyperparameter optimization in deep multi-target prediction.

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

CoRR, 2022

On Calibration of Ensemble-Based Credal Predictors.

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

CoRR, 2022

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

Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

CoRR, 2022

CpG Transformer for imputation of single-cell methylomes.

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

Bioinform., 2022

Set-valued prediction in hierarchical classification with constrained representation complexity.

Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation.

Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

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

Eyke Hüllermeier, Willem Waegeman

Mach. Learn., 2021

Efficient set-valued prediction in multi-class classification.

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

Data Min. Knowl. Discov., 2021

Well-calibrated prediction intervals for regression problems.

Nicolas Dewolf, Bernard De Baets, Willem Waegeman

CoRR, 2021

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

Dimitrios Iliadis, Bernard De Baets, Willem Waegeman

CoRR, 2021

Explainability in transformer models for functional genomics.

Jim Clauwaert, Gerben Menschaert, Willem Waegeman

Briefings Bioinform., 2021

2020

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

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

Briefings Bioinform., 2020

2019

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

Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier

Data Min. Knowl. Discov., 2019

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

Eyke Hüllermeier, Willem Waegeman

CoRR, 2019

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

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

CoRR, 2019

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

Mieke Deschepper, Kristof Eeckloo, Dirk Vogelaers, Willem Waegeman

Comput. Methods Programs Biomed., 2019

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

Christina Papagiannopoulou, René Parchen, Willem Waegeman

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Set-Valued Prediction in Multi-Class Classification.

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

Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

2018

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

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

Neural Comput., 2018

Deep F-Measure Maximization in Multi-label Classification: A Comparative Study.

Stijn Decubber, Thomas Mortier, Krzysztof Dembczynski, Willem Waegeman

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

2017

Analyzing Granger Causality in Climate Data with Time Series Classification Methods.

Christina Papagiannopoulou, Stijn Decubber, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Willem Waegeman

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

A non-linear data-driven approach to reveal global vegetation sensitivity to climate.

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

Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, 2017

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 E. C. Verhoest

Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, 2017

2016

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

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

Data Min. Knowl. Discov., 2016

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

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

CoRR, 2016

Consistency of Probabilistic Classifier Trees.

Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

2015

Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels.

Tapio Pahikkala, Markus Viljanen, Antti Airola, Willem Waegeman

CoRR, 2015

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

E. Colman, Willem Waegeman, Bernard De Baets, Veerle Fievez

Comput. Electron. Agric., 2015

2014

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, Willem Waegeman

IEEE ACM Trans. Comput. Biol. Bioinform., 2014

On the bayes-optimality of F-measure maximizers.

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

J. Mach. Learn. Res., 2014

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

Jan Peters, Willem Waegeman, Jasper Van doninck, Els I. Ducheyne, Carlos Calvete, J. Lucientes, Niko E. C. Verhoest, Bernard De Baets

Ecol. Informatics, 2014

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

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

2013

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

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

Mach. Learn., 2013

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

Environ. Model. Softw., 2013

On the Bayes-optimality of F-measure maximizers.

Willem Waegeman, Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier

CoRR, 2013

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

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

A Kernel-Based Framework for Learning Graded Relations From Data.

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

IEEE Trans. Fuzzy Syst., 2012

On label dependence and loss minimization in multi-label classification.

Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

Mach. Learn., 2012

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

Jan Verwaeren, Willem Waegeman, Bernard De Baets

Comput. Stat. Data Anal., 2012

F-Measure Maximization in Topical Classification.

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

Proceedings of the Rough Sets and Current Trends in Computing, 2012

Label Ranking with Partial Abstention based on Thresholded Probabilistic Models.

Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

An Analysis of Chaining in Multi-Label Classification.

Krzysztof Dembczynski, Willem Waegeman, Eyke Hüllermeier

Proceedings of the ECAI 2012, 2012

2011

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

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

Fuzzy Sets Syst., 2011

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

Comput. Stat. Data Anal., 2011

On the ERA ranking representability of pairwise bipartite ranking functions.

Willem Waegeman, Bernard De Baets

Artif. Intell., 2011

An Exact Algorithm for F-Measure Maximization.

Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

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

Willem Waegeman, Bernard De Baets

Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011

A discussion on the accuracy-complexity relationship in modelling fish habitat preference using genetic Takagi-Sugeno fuzzy systems.

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

Proceedings of the 5th IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems, 2011

Learning Valued Relations from Data.

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

Proceedings of the Eurofuse 2011, 2011

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

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

Proceedings of the Eurofuse 2011, 2011

2010

A comparison of AUC estimators in small-sample studies.

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

Proceedings of the third International Workshop on Machine Learning in Systems Biology, 2010

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

Willem Waegeman, Bernard De Baets

Inf. Sci., 2010

Learning intransitive reciprocal relations with kernel methods.

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

Eur. J. Oper. Res., 2010

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 Bioinform., 2010

Conditional Ranking on Relational Data.

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

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss.

Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

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

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

Proceedings of the Second World Congress on Nature & Biologically Inspired Computing, 2010

Directional predictions for 4-class BCI data.

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

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

A Survey on ROC-based Ordinal Regression.

Willem Waegeman, Bernard De Baets

Proceedings of the Preference Learning., 2010

2009

Kernel-based learning methods for preference aggregation.

Willem Waegeman, Bernard De Baets, Luc Boullart

4OR, 2009

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

Willem Waegeman

4OR, 2009

2008

ROC analysis in ordinal regression learning.

Willem Waegeman, Bernard De Baets, Luc Boullart

Pattern Recognit. Lett., 2008

Learning layered ranking functions with structured support vector machines.

Willem Waegeman, Bernard De Baets, Luc Boullart

Neural Networks, 2008

Classifying carpets based on laser scanner data.

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

Eng. Appl. Artif. Intell., 2008

On the scalability of ordered multi-class ROC analysis.

Willem Waegeman, Bernard De Baets, Luc Boullart

Comput. Stat. Data Anal., 2008