Forests of Randomized Shapelet Trees
2015 (English)In: Statistical learning and data sciences, Third International Symposium, Proceedings / [ed] Alexander Gammerman, Vladimir Vovk, Harris Papadopoulos, Berlin: Springer Publishing Company, 2015, 126-136 p.Conference paper (Refereed)
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 the technique 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.
Place, publisher, year, edition, pages
Berlin: Springer Publishing Company, 2015. 126-136 p.
, Lecture notes in artificial intelligence, ISSN 1611-3349 ; 9047
data series classification, shapelets, decision trees, ensemble
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-122820ISBN: 978-3-319-17091-6OAI: oai:DiVA.org:su-122820DiVA: diva2:868623
Statistical learning and data sciences, Third International Symposium, SLDS 2015 Egham, UK, April 20–23, 2015 Proceedings