Forests of Randomized Shapelet Trees

  • Isak KarlssonEmail author
  • Panagotis Papapetrou
  • Henrik Boström
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9047)


Shapelets have recently been proposed for data series classification, due to their ability to capture phase independent and local information. Decision trees based on shapelets have been shown to provide not only interpretable models, but also, in many cases, state-of-the-art predictive performance. Shapelet discovery is, however, computationally costly, and although several techniques for speeding up this task have been proposed, the computational cost is still in many cases prohibitive. In this work, an ensemble-based method, referred to as Random Shapelet Forest (RSF), is proposed, which builds on the success of the random forest algorithm, and which is shown to have a lower computational complexity than the original shapelet tree learning algorithm. An extensive empirical investigation shows that the algorithm provides competitive predictive performance and that a proposed way of calculating importance scores can be used to successfully identify influential regions.


Data series classification Shapelets Decision trees  Ensemble 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Isak Karlsson
    • 1
    Email author
  • Panagotis Papapetrou
    • 1
  • Henrik Boström
    • 1
  1. 1.Department of Computer and Systems SciencesStockholm UniversityKistaSweden

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