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

Anthony Bagnall; Jason Lines; Jon Hills; Aaron Bostrom

doi:10.1109/ICDE.2016.7498418

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

Anthony Bagnall, Jason Lines, Jon Hills, Aaron Bostrom

Research output: Contribution to conference › Abstract › peer-review

40 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 language	English
Pages	1548-1549
Number of pages	2
DOIs	https://doi.org/10.1109/ICDE.2016.7498418
Publication status	Published - May 2016
Event	32nd International Conference on Data Engineering (ICDE) - Helsinki, Finland Duration: 16 May 2016 → 20 May 2016

Conference

Conference	32nd International Conference on Data Engineering (ICDE)
Country/Territory	Finland
City	Helsinki
Period	16/05/16 → 20/05/16

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10.1109/ICDE.2016.7498418

Tony Bagnall
- Anthony.Bagnalluea.acuk
- School of Computing Sciences - Honorary Professorial Fellow
Person: Honorary
Jason Lines
- J.Linesuea.acuk
- School of Computing Sciences - Associate Professor in Computing Sciences
- Data Science and AI - Member
- Smart Emerging Technologies - Member
Person: Research Group Member, Academic, Teaching & Research

Cite this

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title = "Time-series classification with COTE: The collective of transformation-based ensembles",

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.",

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AU - Bagnall, Anthony

AU - Lines, Jason

AU - Hills, Jon

AU - Bostrom, Aaron

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Y1 - 2016/5

N2 - 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.

AB - 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.

U2 - 10.1109/ICDE.2016.7498418

DO - 10.1109/ICDE.2016.7498418

M3 - Abstract

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T2 - 32nd International Conference on Data Engineering (ICDE)

Y2 - 16 May 2016 through 20 May 2016

ER -

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

Abstract

Conference

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Profiles

Tony Bagnall

Jason Lines

Cite this