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

Bagnall, Anthony, Lines, Jason ORCID: https://orcid.org/0000-0002-1496-5941, Hills, Jon and Bostrom, Aaron ORCID: https://orcid.org/0000-0002-7300-6038 (2016) Time-series classification with COTE: The collective of transformation-based ensembles. In: 32nd International Conference on Data Engineering (ICDE), 2016-05-16 - 2016-05-20.

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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.

Item Type: Conference or Workshop Item (Other)
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 24 Sep 2016 01:06
Last Modified: 21 Oct 2022 23:42
URI: https://ueaeprints.uea.ac.uk/id/eprint/60484
DOI: 10.1109/ICDE.2016.7498418

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