On the Role of Maximal Independent Sets in Cleaning Data Sets for Supervised Ranking. - Michaël Rademaker; Bernard De Baets; Hans E. De Meyer (FUZZ-IEEE, 2006)

Directional Adjustment of Average Rankings of Two Posets: an Application to Herb Layer and Moss Layer Pollution in Baden-Württemberg. - Michaël Rademaker; Bernard De Baets; Hans E. De Meyer; Rainer Brüggemann (EnviroInfo (2), 2007)

Improving tractability of group decision making on environmental problems through the use of social intensities of preferences. - Kamran Zendehdel; Michaël Rademaker; Bernard De Baets; Guido Van Huylenbroeck (Environ. Model. Softw., 2009)

Loss optimal monotone relabeling of noisy multi-criteria data sets. - Michaël Rademaker; Bernard De Baets; Hans E. De Meyer (Inf. Sci., 2009)

A threshold for majority in the context of aggregating partial order relations. - Michaël Rademaker; Bernard De Baets (FUZZ-IEEE, 2010)

Context-dependent environmental sound monitoring using SOM coupled with LEGION. - Damiano Oldoni; Bert De Coensel; Michaël Rademaker; Bernard De Baets; Dick Botteldooren (IJCNN, 2010)

Predicting Partial Orders: Ranking with Abstention. - Weiwei Cheng; Michaël Rademaker; Bernard De Baets; Eyke Hüllermeier (ECML/PKDD (1), 2010)

Aggregation of monotone reciprocal relations with application to group decision making. - Michaël Rademaker; Bernard De Baets (Fuzzy Sets Syst., 2011)

Optimal restoration of stochastic monotonicity with respect to cumulative label frequency loss functions. - Michaël Rademaker; Bernard De Baets (Inf. Sci., 2011)

Consistent union and prioritized consistent union: new operations for preference aggregation. - Michaël Rademaker; Bernard De Baets (Ann. Oper. Res., 2012)

Optimal monotone relabelling of partially non-monotone ordinal data. - Michaël Rademaker; Bernard De Baets; Hans E. De Meyer (Optim. Methods Softw., 2012)

Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen. - Matteo Reggente; Jan Peters 002; Jan Theunis; Martine Van Poppel; Michaël Rademaker; Prashant Kumar 002; Bernard De Baets (Environ. Model. Softw., 2014)

A ranking procedure based on a natural monotonicity constraint. - Michaël Rademaker; Bernard De Baets (Inf. Fusion, 2014)

Adding feasibility constraints to a ranking rule under a monotonicity constraint. - Raúl Pérez-Fernández; Michaël Rademaker; Pedro Alonso 001; Irene Díaz; Bernard De Baets (IFSA-EUSFLAT, 2015)

An interval estimator for the unmixing of mixtures with set-based source descriptions. - Jan Verwaeren; Michaël Rademaker; Bernard De Baets (Int. J. Approx. Reason., 2015)

Representations of votes facilitating monotonicity-based ranking rules: From votrix to votex. - Raúl Pérez-Fernández; Michaël Rademaker; Pedro Alonso 001; Irene Díaz; Susana Montes; Bernard De Baets (Int. J. Approx. Reason., 2016)

The scorix: A popular representation of votes revisited. - Raúl Pérez-Fernández; Michaël Rademaker; Bernard De Baets (Int. J. Approx. Reason., 2016)

Monotonicity-based ranking on the basis of multiple partially specified reciprocal relations. - Raúl Pérez-Fernández; Michaël Rademaker; Pedro Alonso 001; Irene Díaz; Susana Montes; Bernard De Baets (Fuzzy Sets Syst., 2017)

Monometrics and their role in the rationalisation of ranking rules. - Raúl Pérez-Fernández; Michaël Rademaker; Bernard De Baets (Inf. Fusion, 2017)

A correspondence between voting procedures and stochastic orderings. - Ignacio Montes; Michaël Rademaker; Raúl Pérez-Fernández; Bernard De Baets (Eur. J. Oper. Res., 2020)

Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring. - M. Jeroen Van Der Donckt; Jonas Van Der Donckt; Emiel Deprost; Nicolas Vandenbussche; Michaël Rademaker; Gilles Vandewiele; Sofie Van Hoecke (CoRR, 2022)

MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB. - Jeroen Van Der Donckt; Jonas Van Der Donckt; Michaël Rademaker; Sofie Van Hoecke (IEEE VIS (Short Papers), 2023)

Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines. - Jonas Van Der Donckt; M. Jeroen Van Der Donckt; Michaël Rademaker; Sofie Van Hoecke (CoRR, 2023)

MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB. - M. Jeroen Van Der Donckt; Jonas Van Der Donckt; Michaël Rademaker; Sofie Van Hoecke (CoRR, 2023)

Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring. - M. Jeroen Van Der Donckt; Jonas Van Der Donckt; Emiel Deprost; Nicolas Vandenbussche; Michaël Rademaker; Gilles Vandewiele; Sofie Van Hoecke (Biomed. Signal Process. Control., 2023)

A semi-supervised anomaly detection approach for detecting mechanical failures. - Colin Soete; Michaël Rademaker; Sofie Van Hoecke (Artif. Intell. Eng. Des. Anal. Manuf., 2024)