文件名称:classbaseattrbutetimeclassification
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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.
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.
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class base attribute time classification.pdf