Binary Shapelet Transform for Multiclass Time Series Classification

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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.
Original languageEnglish
Title of host publicationBig Data Analytics and Knowledge Discovery
Subtitle of host publication17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings
EditorsSanjay Madria, Takahiro Hara
ISBN (Electronic)978-3-319-22729-0
ISBN (Print)978-3-319-22728-3
Publication statusPublished - 1 Sep 2015
EventDaWaK 2015 : 17th International Conference on Big Data Analytics and Knowledge Discovery - Valencia, Spain
Duration: 1 Sep 20154 Sep 2015

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceDaWaK 2015 : 17th International Conference on Big Data Analytics and Knowledge Discovery


  • shapelets
  • Time Series Classification

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