Time-series classification with COTE: The collective of transformation-based ensembles

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38 Citations (Scopus)


We have proposed an ensemble scheme for TSC based on constructing classifiers on different data representations. The standard baseline algorithms used in TSC research are 1-NN with Euclidean distance and/or Dynamic Time Warping. We have conclusively shown that COTE significantly out-performs both of these approaches, and that COTE it is significantly better than all of the competing algorithms that have been proposed in the literature. We believe the results we present represents a new state-of-the-art in TSC that new algorithms should be compared to in terms of accuracy.
Original languageEnglish
Number of pages2
Publication statusPublished - May 2016
Event32nd International Conference on Data Engineering (ICDE) - Helsinki, Finland
Duration: 16 May 201620 May 2016


Conference32nd International Conference on Data Engineering (ICDE)

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