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M. 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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