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COST-STSM-IC0903-12676 Behavioral Classification of Oystercatchers by Combining Interactive Visualization and Machine Learning. Rudolf Netzel. Motivation. The classification of animal movement data is an important Basis for many physiological, evolutionary, energetic (etc.) inferences
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COST-STSM-IC0903-12676Behavioral Classification of Oystercatchers byCombining Interactive Visualization and Machine Learning Rudolf Netzel
Motivation • The classification of animal movement data is an important • Basis for many physiological, evolutionary, energetic (etc.) inferences • Labels are only obtained during observation • Huge amount of data • Only few labeled data • Currently statistical analyses dominate • Interactive visualization of spatio- temporal data • Support labeling • Evaluation of classification models Oystercatcher [Wiki Media]
STSM • 22.04.2013 – 26.04.2013 • Follow-up cooperation due to results of the Dagstuhlseminar • Host: Emiel van Loon, University of Amsterdam • Institute for Biodiversity and Ecosystem Dynamics • Ali Soleymani, University of Zürich • Signal processing background • Apply a segmentation • Modify feature vector with information form segmentation • Rudolf Netzel, University of Stuttgart • Similarity to thesis “Interactive Learning” • Creating a visualization for data labeling • Interactive
Data • GPS • at non observation time • ~ every 10 - 30 min, • at observation time • ~ every 15s – 60s • Accelerometer • 20 Hz over 3 seconds (up to 60 measures per gps fix) • Used to derive model parameter
Framework Requirements • Interactive visualization of spatio- temporal data • Support labeling • Evaluation of classification models • Additional preferences of domain experts • Easy labeling • Plots of arbitrary attributes • Parallel Coordinate • 2D Scatter Plots • Comparison of multiple models • Location of frequent disagreement should be visible • Differences in observed versus model behavior
Framework – Functionalities Overview • Project management • Specification of color mappings • Selection / unselection • Plots • Navigation • Brushing & Linking • Glyph to represent class labels Observation label Model 1 Model 3 Model 2
Framework - Component Overview • GPS location map • Relabeling • 2D scatter plot • Parallel Coordinates
Data Selection • Single and area selection • Zooming • Panning
Data Selection – Trajectory Mode • Highlight object trajectory • N next points on an object trajectory
Labeling • Label selected gps positions • Update of gps map
2D Scatter Plot • Selection of arbitrary models and parameters for x und y axis • Color encodes the object ID • Lines indicate a temporal correlation • Highlighting of sub selected positions in gps map and Labeling View
Parallel Coordinates • Selection of arbitrary parameters form models • Color encodes object ID
Conclusion • Behavior classification is important for inferences • Large difference between labeled and unlabeled data • Requirements of a framework that should support the labeling • Components of the framework so fare Additional Work • Classifier retraining • WEKA • Run external java code • Display density or a map of gps fixes • …
