M. Jeroen Van Der Donckt

16 papers B 1Journal 11Unranked 4
YearRankTypeTitle / Venue / Authors
2023 J jnl
Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines.
CoRR
Jonas Van Der Donckt, M. Jeroen Van Der Donckt, Michaël Rademaker, Sofie Van Hoecke
2023 J jnl
Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring.
Biomed. Signal Process. Control.
M. Jeroen Van Der Donckt, Jonas Van Der Donckt, Emiel Deprost, Nicolas Vandenbussche, Michaël Rademaker, Gilles Vandewiele, Sofie Van Hoecke
2023 J jnl
MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB.
CoRR
M. Jeroen Van Der Donckt, Jonas Van Der Donckt, Michaël Rademaker, Sofie Van Hoecke
2023 J jnl
tsdownsample: high-performance time series downsampling for scalable visualization.
CoRR
M. Jeroen Van Der Donckt, Jonas Van Der Donckt, Sofie Van Hoecke
2022 — conf
Data Analytics for Health and Connected Care: Ontology, Knowledge Graph and Applications.
PervasiveHealth
Bram Steenwinckel, Mathias De Brouwer, Marija Stojchevska, M. Jeroen Van Der Donckt, Jelle Nelis, Joeri Ruyssinck, Joachim van der Herten, Koen Casier, Jan Van Ooteghem, Pieter Crombez, Filip De Turck, Sofie Van Hoecke, Femke Ongenae
2022 J jnl
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems.
CoRR
Danny Weyns, Omid Gheibi, Federico Quin, M. Jeroen Van Der Donckt
2022 J jnl
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems.
ACM Trans. Auton. Adapt. Syst.
Danny Weyns, Omid Gheibi, Federico Quin, M. Jeroen Van Der Donckt
2022 J jnl
Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring.
CoRR
M. Jeroen Van Der Donckt, Jonas Van Der Donckt, Emiel Deprost, Nicolas Vandenbussche, Michaël Rademaker, Gilles Vandewiele, Sofie Van Hoecke
2022 J jnl
Plotly-Resampler: Effective Visual Analytics for Large Time Series.
CoRR
Jonas Van Der Donckt, M. Jeroen Van Der Donckt, Emiel Deprost, Sofie Van Hoecke
2022 — conf
Powershap: A Power-Full Shapley Feature Selection Method.
ECML/PKDD (1)
Jarne Verhaeghe, M. Jeroen Van Der Donckt, Femke Ongenae, Sofie Van Hoecke
2022 J jnl
Powershap: A Power-full Shapley Feature Selection Method.
CoRR
Jarne Verhaeghe, M. Jeroen Van Der Donckt, Femke Ongenae, Sofie Van Hoecke
2022 J jnl
tsflex: Flexible time series processing & feature extraction.
SoftwareX
Jonas Van Der Donckt, M. Jeroen Van Der Donckt, Emiel Deprost, Sofie Van Hoecke
2021 J jnl
tsflex: flexible time series processing & feature extraction.
CoRR
Jonas Van Der Donckt, M. Jeroen Van Der Donckt, Emiel Deprost, Sofie Van Hoecke
2020 — conf
Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals.
SEAMS@ICSE
M. Jeroen Van Der Donckt, Danny Weyns, Federico Quin, Jonas Van Der Donckt, Sam Michiels
2018 B conf
Cost-Benefit Analysis at Runtime for Self-adaptive Systems Applied to an Internet of Things Application.
ENASE
M. Jeroen Van Der Donckt, Danny Weyns, M. Usman Iftikhar, Ritesh Kumar Singh
2018 — conf
Effective Decision Making in Self-adaptive Systems Using Cost-Benefit Analysis at Runtime and Online Learning of Adaptation Spaces.
ENASE (Selected Papers)
M. Jeroen Van Der Donckt, Danny Weyns, M. Usman Iftikhar, Sarpreet Singh Buttar