53 citations · 75 across the 6 of their papers we have counts for
6 papers
MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB
Jeroen Van Der Donckt, Jonas Van Der Donckt, Michael Rademaker +1
Visualization plays an important role in analyzing and exploring time series data. To facilitate efficient visualization of large datasets, downsampling has emerged as a well-estab…
Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines
Jonas Van Der Donckt, Jeroen Van Der Donckt, Michael Rademaker +1
Time series visualization plays a crucial role in identifying patterns and extracting insights across various domains. However, as datasets continue to grow in size, visualizing th…
Powershap: A Power-full Shapley Feature Selection Method
Jarne Verhaeghe, Jeroen Van Der Donckt, Femke Ongenae +1
Feature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper m…
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
Jeroen Van Der Donckt, Jonas Van Der Donckt, Emiel Deprost +4
Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches a…
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems
Danny Weyns, Omid Gheibi, Federico Quin +1
Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation thes…
tsflex: flexible time series processing & feature extraction
Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost +1
Time series processing and feature extraction are crucial and time-intensive steps in conventional machine learning pipelines. Existing packages are limited in their applicability,…