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

Research output: Contribution to conferenceAbstract

18 Citations (Scopus)

Abstract

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
Pages1548-1549
Number of pages2
DOIs
Publication statusPublished - May 2016
Event32nd International Conference on Data Engineering (ICDE) - Helsinki, Finland
Duration: 16 May 201620 May 2016

Conference

Conference32nd International Conference on Data Engineering (ICDE)
CountryFinland
CityHelsinki
Period16/05/1620/05/16

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