标题:XG-SF: An XGBoost Classifier Based on Shapelet Features for Time Series Classification
作者:Ji, Cun; Zou, Xiunan; Hu, Yupeng; Liu, Shijun; Lyu, Lei; Zheng, Xiangwei
通讯作者:Ji, C;Ji, C
作者机构:[Ji, Cun; Zou, Xiunan; Lyu, Lei; Zheng, Xiangwei] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China.; [Hu, Yupe 更多
会议名称:7th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)
会议日期:OCT 19-21, 2018
来源:2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS
出版年:2019
卷:147
页码:24-28
DOI:10.1016/j.procs.2019.01.179
关键词:time series classification; XGBoost; shapelet feature
摘要: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.
收录类别:CPCI-S
WOS核心被引频次:1
资源类型:会议论文
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