On the scalability of ordered multi-class ROC analysis. - Willem Waegeman; Bernard De Baets; Luc Boullart (Comput. Stat. Data Anal., 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)

Learning layered ranking functions with structured support vector machines. - Willem Waegeman; Bernard De Baets; Luc Boullart (Neural Networks, 2008)

ROC analysis in ordinal regression learning. - Willem Waegeman; Bernard De Baets; Luc Boullart (Pattern Recognit. Lett., 2008)

Learning to rank: a ROC-based graph-theoretic approach. - Willem Waegeman (4OR, 2009)

Kernel-based learning methods for preference aggregation. - Willem Waegeman; Bernard De Baets; Luc Boullart (4OR, 2009)

A Survey on ROC-based Ordinal Regression. - Willem Waegeman; Bernard De Baets (Preference Learning, 2010)

Directional predictions for 4-class BCI data. - Dieter Devlaminck; Willem Waegeman; Bruno Bauwens; Bart Wyns; Georges Otte; Luc Boullart; Patrick Santens (ESANN, 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 (NaBIC, 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 (ECML/PKDD (1), 2010)

Conditional Ranking on Relational Data. - Tapio Pahikkala; Willem Waegeman; Antti Airola; Tapio Salakoski; Bernard De Baets (ECML/PKDD (2), 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)

Learning intransitive reciprocal relations with kernel methods. - Tapio Pahikkala; Willem Waegeman; Evgeni Tsivtsivadze; Tapio Salakoski; Bernard De Baets (Eur. J. Oper. Res., 2010)

A transitivity analysis of bipartite rankings in pairwise multi-class classification. - Willem Waegeman; Bernard De Baets (Inf. Sci., 2010)

A comparison of AUC estimators in small-sample studies. - Antti Airola; Tapio Pahikkala; Willem Waegeman; Bernard De Baets; Tapio Salakoski (MLSB, 2010)

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 (Eurofuse, 2011)

Learning Valued Relations from Data. - Willem Waegeman; Tapio Pahikkala; Antti Airola; Tapio Salakoski; Bernard De Baets (Eurofuse, 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 (GEFS, 2011)

ERA ranking representability: The missing link between ordinal regression and multi-class classification. - Willem Waegeman; Bernard De Baets (ISDA, 2011)

An Exact Algorithm for F-Measure Maximization. - Krzysztof Dembczynski; Willem Waegeman; Weiwei Cheng; Eyke Hüllermeier (NIPS, 2011)

On the ERA ranking representability of pairwise bipartite ranking functions. - Willem Waegeman; Bernard De Baets (Artif. Intell., 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)

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 Analysis of Chaining in Multi-Label Classification. - Krzysztof Dembczynski; Willem Waegeman; Eyke Hüllermeier (ECAI, 2012)

Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. - Weiwei Cheng; Eyke Hüllermeier; Willem Waegeman; Volkmar Welker (NIPS, 2012)

F-Measure Maximization in Topical Classification. - Weiwei Cheng; Krzysztof Dembczynski; Eyke Hüllermeier; Adrian Jaroszewicz; Willem Waegeman (RSCTC, 2012)

Learning partial ordinal class memberships with kernel-based proportional odds models. - Jan Verwaeren; Willem Waegeman; Bernard De Baets (Comput. Stat. Data Anal., 2012)

On label dependence and loss minimization in multi-label classification. - Krzysztof Dembczynski; Willem Waegeman; Weiwei Cheng; Eyke Hüllermeier (Mach. Learn., 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)

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 (ICML (3), 2013)

On the Bayes-optimality of F-measure maximizers. - Willem Waegeman; Krzysztof Dembczynski; Weiwei Cheng; Eyke Hüllermeier (CoRR, 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)

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)

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 (ECML/PKDD (2), 2014)

Predicting spatio-temporal Culicoides imicola distributions in Spain based on environmental habitat characteristics and species dispersal. - Jan Peters 002; Willem Waegeman; Jasper Van doninck; Els I. Ducheyne; Carlos Calvete; J. Lucientes; Niko E. C. Verhoest; Bernard De Baets (Ecol. Informatics, 2014)

On the bayes-optimality of F-measure maximizers. - Willem Waegeman; Krzysztof Dembczynski; Arkadiusz Jachnik; Weiwei Cheng; Eyke Hüllermeier (J. Mach. Learn. Res., 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)

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)

Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels. - Tapio Pahikkala; Markus Viljanen; Antti Airola; Willem Waegeman (CoRR, 2015)

Consistency of Probabilistic Classifier Trees. - Krzysztof Dembczynski; Wojciech Kotlowski; Willem Waegeman; Róbert Busa-Fekete; Eyke Hüllermeier (ECML/PKDD (2), 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)

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)

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 (MultiTemp, 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 (MultiTemp, 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 (ECML/PKDD (3), 2017)

Deep F-Measure Maximization in Multi-label Classification: A Comparative Study. - Stijn Decubber; Thomas Mortier; Krzysztof Dembczynski; Willem Waegeman (ECML/PKDD (1), 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)

Set-Valued Prediction in Multi-Class Classification. - Thomas Mortier; Marek Wydmuch; Krzysztof Dembczynski; Eyke Hüllermeier; Willem Waegeman (BNAIC/BENELEARN, 2019)

Investigating Time Series Classification Techniques for Rapid Pathogen Identification with Single-Cell MALDI-TOF Mass Spectrum Data. - Christina Papagiannopoulou; René Parchen; Willem Waegeman (ECML/PKDD (3), 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)

Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification. - Thomas Mortier; Marek Wydmuch; Eyke Hüllermeier; Krzysztof Dembczynski; Willem Waegeman (CoRR, 2019)

Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction. - Eyke Hüllermeier; Willem Waegeman (CoRR, 2019)

Multi-target prediction: a unifying view on problems and methods. - Willem Waegeman; Krzysztof Dembczynski; Eyke Hüllermeier (Data Min. Knowl. Discov., 2019)

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)

Explainability in transformer models for functional genomics. - Jim Clauwaert; Gerben Menschaert; Willem Waegeman (Briefings Bioinform., 2021)

Automated problem setting selection in multi-target prediction with AutoMTP. - Dimitrios Iliadis; Bernard De Baets; Willem Waegeman (CoRR, 2021)

Well-calibrated prediction intervals for regression problems. - Nicolas Dewolf; Bernard De Baets; Willem Waegeman (CoRR, 2021)

Efficient set-valued prediction in multi-class classification. - Thomas Mortier; Marek Wydmuch; Krzysztof Dembczynski; Eyke Hüllermeier; Willem Waegeman (Data Min. Knowl. Discov., 2021)

Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. - Eyke Hüllermeier; Willem Waegeman (Mach. Learn., 2021)

Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. - Viktor Bengs; Eyke Hüllermeier; Willem Waegeman (NeurIPS, 2022)

Set-valued prediction in hierarchical classification with constrained representation complexity. - Thomas Mortier; Eyke Hüllermeier; Krzysztof Dembczynski; Willem Waegeman (UAI, 2022)

CpG Transformer for imputation of single-cell methylomes. - Gaetan De Waele; Jim Clauwaert; Gerben Menschaert; Willem Waegeman (Bioinform., 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)

On Calibration of Ensemble-Based Credal Predictors. - Thomas Mortier; Viktor Bengs; Eyke Hüllermeier; Stijn Luca; Willem Waegeman (CoRR, 2022)

Hyperparameter optimization in deep multi-target prediction. - Dimitrios Iliadis; Marcel Wever; Bernard De Baets; Willem Waegeman (CoRR, 2022)

Multi-target prediction for dummies using two-branch neural networks. - Dimitrios Iliadis; Bernard De Baets; Willem Waegeman (Mach. Learn., 2022)

Novel Transformer Networks for Improved Sequence Labeling in genomics. - Jim Clauwaert; Willem Waegeman (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

On the Calibration of Probabilistic Classifier Sets. - Thomas Mortier; Viktor Bengs; Eyke Hüllermeier; Stijn Luca; Willem Waegeman (AISTATS, 2023)

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. - Viktor Bengs; Eyke Hüllermeier; Willem Waegeman (ICML, 2023)

Valid prediction intervals for regression problems. - Nicolas Dewolf; Bernard De Baets; Willem Waegeman (Artif. Intell. Rev., 2023)

Heteroskedastic conformal regression. - Nicolas Dewolf; Bernard De Baets; Willem Waegeman (CoRR, 2023)

DeepMTP: A Python-based deep learning framework for multi-target prediction. - Dimitrios Iliadis; Bernard De Baets; Willem Waegeman (SoftwareX, 2023)

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? - Mira Jürgens; Nis Meinert; Viktor Bengs; Eyke Hüllermeier; Willem Waegeman (ICML, 2024)

Hyperparameter optimization of two-branch neural networks in multi-target prediction. - Dimitrios Iliadis; Marcel Wever; Bernard De Baets; Willem Waegeman (Appl. Soft Comput., 2024)

Assessment of Uncertainty Quantification in Universal Differential Equations. - Nina Schmid; David Fernandes del Pozo; Willem Waegeman; Jan Hasenauer (CoRR, 2024)

A comparison of embedding aggregation strategies in drug-target interaction prediction. - Dimitrios Iliadis; Bernard De Baets; Tapio Pahikkala; Willem Waegeman (BMC Bioinform., 2024)

Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction. - Morteza Rakhshaninejad; Mira Jürgens; Nicolas Dewolf; Willem Waegeman (COPA, 2025)

Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics. - Lauren Theunissen; Thomas Mortier; Yvan Saeys; Willem Waegeman (Briefings Bioinform., 2025)

Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction. - Gaetan De Waele; Gerben Menschaert; Peter Vandamme; Willem Waegeman (Comput. Biol. Medicine, 2025)

Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks. - Regina Ibragimova; Dimitrios Iliadis; Willem Waegeman (J. Chem. Inf. Model., 2025)

Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition. - Arthur Hoarau; Benjamin Quost; Sébastien Destercke; Willem Waegeman (CoRR, 2025)

Conformal Prediction in Hierarchical Classification. - Thomas Mortier; Alireza Javanmardi; Yusuf Sale; Eyke Hüllermeier; Willem Waegeman (CoRR, 2025)

A calibration test for evaluating set-based epistemic uncertainty representations. - Mira Jürgens; Thomas Mortier; Eyke Hüllermeier; Viktor Bengs; Willem Waegeman (CoRR, 2025)

Optimal Conformal Prediction under Epistemic Uncertainty. - Alireza Javanmardi; Soroush H. Zargarbashi; Santo M. A. R. Thies; Willem Waegeman; Aleksandar Bojchevski; Eyke Hüllermeier (CoRR, 2025)

Why Machine Learning Models Fail to Fully Capture Epistemic Uncertainty. - Sebastián Jiménez; Mira Jürgens; Willem Waegeman (CoRR, 2025)