Support Page for "Extracting Statistical Graph Features for Accurate and Efficient Time Series Classification"
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We show examples of how features extracted from MVG can help produce visual cues about classification results. Note that all samples in the test dataset are drawn as line plots in per-class charts. Only ten most important features are shown in scatter matrices.
Dataset: Computers
All samples from test dataset
Scatter matrix of ten most important MVG features
Dataset: FordA
All samples from test dataset
Scatter matrix of ten most important MVG features
Dataset: FordB
All samples from test dataset
Scatter matrix of ten most important MVG features
Dataset: Meat
All samples from test dataset
Scatter matrix of ten most important MVG features
Dataset: SmallKitchenAppliances
All samples from test dataset
Scatter matrix of ten most important MVG features
Dataset: Worms
All samples from test dataset
Scatter matrix of ten most important MVG features
Dataset: WormsTwoClass
All samples from test dataset
Scatter matrix of ten most important MVG features