Binary shapelet transform for multiclass time series classification (extended version)

Bostrom, Aaron and Bagnall, Anthony (2017) Binary shapelet transform for multiclass time series classification (extended version). In: Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXII. Lecture Notes in Computer Science, 10420 (1). Springer, pp. 24-46. ISBN 978-3-662-55607-8

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Abstract

Shapelets have recently been proposed as a new primitive for time series classification. Shapelets are subseries of series that best split the data into its classes. In the original research, shapelets were found recursively within a decision tree through enumeration of the search space. Subsequent research indicated that using shapelets as the basis for transforming datasets leads to more accurate classifiers. Both these approaches evaluate how well a shapelet splits all the classes. However, often a shapelet is most useful in distinguishing between members of the class of the series it was drawn from against all others. To assess this conjecture, we evaluate a one vs all encoding scheme. This technique simplifies the quality assessment calculations, speeds up the execution through facilitating more frequent early abandon and increases accuracy for multi-class problems. We also propose an alternative shapelet evaluation scheme which we demonstrate significantly speeds up the full search.

Item Type: Book Section
Uncontrolled Keywords: shapelets,time series classification
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 16 Aug 2018 08:30
Last Modified: 29 May 2020 00:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/68064
DOI: 10.1007/978-3-662-55608-5

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