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ORCIDM. Jeroen Van Der Donckt
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2020 – today
- 2023
[j3]M. Jeroen Van Der Donckt
, Jonas Van Der Donckt, Emiel Deprost, Nicolas Vandenbussche, Michaël Rademaker, Gilles Vandewiele, Sofie Van Hoecke:
Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring. Biomed. Signal Process. Control. 81: 104429 (2023)- [i8]Jonas Van Der Donckt, M. Jeroen Van Der Donckt, Michaël Rademaker, Sofie Van Hoecke:
Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines. CoRR abs/2304.00900 (2023) - [i7]M. Jeroen Van Der Donckt, Jonas Van Der Donckt, Michaël Rademaker, Sofie Van Hoecke:
MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB. CoRR abs/2305.00332 (2023) - [i6]M. Jeroen Van Der Donckt, Jonas Van Der Donckt, Sofie Van Hoecke:
tsdownsample: high-performance time series downsampling for scalable visualization. CoRR abs/2307.05389 (2023) - 2022
- [j2]Jonas Van Der Donckt, M. Jeroen Van Der Donckt, Emiel Deprost, Sofie Van Hoecke:
tsflex: Flexible time series processing & feature extraction. SoftwareX 17: 100971 (2022) - [j1]Danny Weyns, Omid Gheibi, Federico Quin, M. Jeroen Van Der Donckt:
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems. ACM Trans. Auton. Adapt. Syst. 17: 1:1-1:42 (2022) - [c5]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:
Data Analytics for Health and Connected Care: Ontology, Knowledge Graph and Applications. PervasiveHealth 2022: 344-360 - [c4]Jarne Verhaeghe, M. Jeroen Van Der Donckt, Femke Ongenae, Sofie Van Hoecke:
Powershap: A Power-Full Shapley Feature Selection Method. ECML/PKDD (1) 2022: 71-87 - [i5]Danny Weyns, Omid Gheibi, Federico Quin, M. Jeroen Van Der Donckt:
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems. CoRR abs/2204.06254 (2022) - [i4]Jarne Verhaeghe, M. Jeroen Van Der Donckt, Femke Ongenae, Sofie Van Hoecke:
Powershap: A Power-full Shapley Feature Selection Method. CoRR abs/2206.08394 (2022) - [i3]Jonas Van Der Donckt, M. Jeroen Van Der Donckt, Emiel Deprost, Sofie Van Hoecke:
Plotly-Resampler: Effective Visual Analytics for Large Time Series. CoRR abs/2206.08703 (2022) - [i2]M. Jeroen Van Der Donckt, Jonas Van Der Donckt, Emiel Deprost, Nicolas Vandenbussche, Michaël Rademaker, Gilles Vandewiele, Sofie Van Hoecke:
Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring. CoRR abs/2207.07753 (2022) - 2021
- [i1]Jonas Van Der Donckt, M. Jeroen Van Der Donckt, Emiel Deprost, Sofie Van Hoecke:
tsflex: flexible time series processing & feature extraction. CoRR abs/2111.12429 (2021) - 2020
- [c3]M. Jeroen Van Der Donckt, Danny Weyns, Federico Quin, Jonas Van Der Donckt, Sam Michiels:
Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals. SEAMS@ICSE 2020: 20-30
2010 – 2019
- 2018
- [c2]M. Jeroen Van Der Donckt, Danny Weyns, M. Usman Iftikhar, Sarpreet Singh Buttar:
Effective Decision Making in Self-adaptive Systems Using Cost-Benefit Analysis at Runtime and Online Learning of Adaptation Spaces. ENASE (Selected Papers) 2018: 373-403 - [c1]M. Jeroen Van Der Donckt, Danny Weyns, M. Usman Iftikhar, Ritesh Kumar Singh:
Cost-Benefit Analysis at Runtime for Self-adaptive Systems Applied to an Internet of Things Application. ENASE 2018: 478-490
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