In this paper, we present two novel class-based weighting methods for the Euclidean nearest neighbor algorithm and compare them with global weighting methods considering empirical results on a widely accepted time series classification benchmark dataset. Our methods provide higher accuracy than every global weighting in nearly half of the cases and they have better overall performance. We conclude that class-based weighting has great potential for improving time series classification accuracy and it might be extended to use with other distance functions than the Euclidean distance.

class base attribute time classification.pdf

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