Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation

1. 1Compare random sampling, farthest-first traversal, and interactive 2D visualization for sample selection
2. 2Evaluate the effectiveness of each method in reducing annotation time and improving label accuracy
3. 3Investigate the impact of interactive 2D visualization on annotator engagement and fatigue

Data scientists and machine learning engineers can benefit from this research as it aims to improve the accuracy of biomedical time-series data annotation, which is crucial for training reliable machine learning models. This can be applied in healthcare settings where accurate predictions are critical.

💡 Interactive 2D visualization can be an effective sample selection strategy for annotating biomedical time-series data, potentially reducing annotation time and improving label accuracy

📊 Improving biomedical time-series data annotation with interactive 2D visualization