Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations-Reference-Cited by-同舟云学术

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Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations

Published:2019-05-21 Issue:4 Volume:33 Page:1183-1222
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Le Nguyen Thach,Gsponer Severin,Ilie Iulia,O’Reilly Martin,Ifrim Georgiana

Funder

Science Foundation Ireland

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s10618-019-00633-3.pdf

Reference44 articles.

1. Bagnall A, Lines J, Hills J, Bostrom A (2015) Time-series classification with cote: the collective of transformation-based ensembles. IEEE Trans Knowl Data Eng 27(9):2522–2535

2. Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Discov 31(3):606–660. https://doi.org/10.1007/s10618-016-0483-9

3. Baydogan MG, Runger G, Tuv E (2013) A bag-of-features framework to classify time series. IEEE Trans Pattern Anal Mach Intell 35(11):2796–2802. https://doi.org/10.1109/TPAMI.2013.72

4. Benavoli A, Corani G, Mangili F (2016) Should we really use post-hoc tests based on mean-ranks? J Mach Learn Res 17(5):1–10

5. Bostrom A, Bagnall A (2015) Binary shapelet transform for multiclass time series classification. In: Madria S, Hara T (eds) Big data analytics and knowledge discovery. Springer International Publishing, Cham, pp 257–269

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