Journal
2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS
Volume 147, Issue -, Pages 24-28Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2019.01.179
Keywords
time series classification; XGBoost; shapelet feature
Funding
- National Natural Science Foundation of China [61872222, 91546203]
- National Key Research and Development Program of China [2017YFA0700601]
- Major Program of Shandong Province Natural Science Foundation [ZR2018ZB0419]
- Key Research and Development Program of Shandong Province [2017CXGC0605, 2017CXGC0604, 2018GGX101019]
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Time series classification (TSC) has attracted significant interest over the past decade. A lot of TSC methods have been proposed. Among these TSC methods, shapelet based methods are promising for they are interpretable, more accurate, and faster than other methods. For this, a lot of acceleration strategies are proposed. However, the accuracies of speedup methods are not ideal. To address these problems, an XGBoost classifier based on shapelet features (XG-SF) is proposed in this work. In XG-SF, an XGBoost classifier based on shapelet features is used to improve classification accuracy. Our experimental results demonstrate that XG-SF is faster than the state-of-the-art classifiers and the classification accuracy rate is also improved to a certain extent. (C) 2019 The Authors. Published by Elsevier B.V.
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