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, Cun
作者机构:[Ji, Cun ;Zou, Xiunan ;Lyu, Lei ;Zheng, Xiangwei ] School of Information Science and Engineering, Shandong Normal University, Jinan; 250014, China;[Ji 更多
会议名称:7th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018
会议日期:October 19, 2018 - October 21, 2018
来源:Procedia Computer Science
出版年:2019
卷:147
页码:24-28
DOI:10.1016/j.procs.2019.01.179
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.
© 2019 The Author(s).
收录类别:EI
资源类型:会议论文
TOP