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Lines, Jason 
ORCID: https://orcid.org/0000-0002-1496-5941, Taylor, Sarah and Bagnall, Anthony
(2016)
HIVE-COTE: The hierarchical vote collective of transformation-based ensembles for time series classification:IEEE International Conference on Data Mining.
In: IEEE ICDM 2016 conference, 2016-12-13 - 2016-12-15.
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Abstract
There have been many new algorithms proposed over the last five years for solving time series classification (TSC) problems. A recent experimental comparison of the leading TSC algorithms has demonstrated that one approach is significantly more accurate than all others over 85 datasets. That approach, the Flat Collective of Transformation-based Ensembles (Flat-COTE), achieves superior accuracy through combining predictions of 35 individual classifiers built on four representations of the data into a flat hierarchy. Outside of TSC, deep learning approaches such as convolutional neural networks (CNN) have seen a recent surge in popularity and are now state of the art in many fields. An obvious question is whether CNNs could be equally transformative in the field of TSC. To test this, we implement a common CNN structure and compare performance to Flat-COTE and a recently proposed time series-specific CNN implementation.We find that Flat-COTE is significantly more accurate than both deep learning approaches on 85 datasets. These results are impressive, but Flat-COTE is not without deficiencies. We improve the collective by adding new components and proposing a modular hierarchical structure with a probabilistic voting scheme that allows us to encapsulate the classifiers built on each transformation. We add two new modules representing dictionary and interval-based classifiers, and significantly improve upon the existing frequency domain classifiers with a novel spectral ensemble. The resulting classifier, the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is significantly more accurate than Flat-COTE and represents a new state of the art for TSC. HIVE-COTE captures more sources of possible discriminatory features in time series and has a more modular, intuitive structure.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | time series classification |
| Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Interactive Graphics and Audio |
| Related URLs: | |
| Depositing User: | Pure Connector |
| Date Deposited: | 29 Sep 2016 12:00 |
| Last Modified: | 24 May 2023 06:01 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/60634 |
| DOI: | 10.1109/ICDM.2016.0133 |
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