Shapelet Extraction Algorithm for Time Series Ordinal Classification

被引：0

作者：

Yang J. ^{[1
,2
,3
]}

Jing S. ^{[1
,2
,3
]}

Zhong Y. ^{[1
,3
]}

机构：

[1] Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu

[2] School of Electronic Information and Artificial Intelligence, Leshan Normal University, Sichuan, Leshan

[3] School of Computer Science and Technology, University of Chinese Academy of Sciences Shijingshan, Beijing

[4] Intelligent Terminal Key Laboratory of Sichuan Province, Sichuan, Yibin

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2023年 / 52卷 / 06期

关键词：

feature evaluation; ordinal classification; Shapelet; time series;

D O I：

10.12178/1001-0548.2022278

中图分类号：

学科分类号：

摘要：

The current Shapelet extraction algorithm for time series ordinal classification, which suffers from low efficiency, needs to figure out the Pearson's correlation coefficient or the Spearman's correlation coefficient between the Euclidean distances and the label distances from time series to Shapelets to evaluate the Shapelets. To handle this problem, this paper first proposes a Shapelet measure CD-Cover (concentration and dominance of coverage) based on the SAX (symbolic aggregate approximation)-represented time series. The measure takes into account both the concentration and the dominance of coverage of a Shapelet on the time series dataset. Secondly, this paper also proposes a Shapelet extraction algorithm based on random sampling. The algorithm uses the Bloom filter to pre-prune Shapelet candidates and employs a strategy of removing self-similar Shapelets to post-prune the extracting results. Experimental results on 11 time series public datasets show that the Shapelet extracted by the proposed algorithm has better ability for ordinal classification than the existing methods, and meanwhile, the computing efficiency of the proposed algorithm is superior to that of the existing methods. © 2023 Univ. of Electronic Science and Technology of China. All rights reserved.

引用

页码：887 / 896

页数：9

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