Segment-Based Features for Time Series Classification

CASIA OpenIR > 模式识别实验室

	Segment-Based Features for Time Series Classification
	Zhang, Zhang2 ; Cheng, Jun 3,4; Li, Jun 1; Bian, Wei 1; Tao, Dacheng 1
发表期刊	COMPUTER JOURNAL
	2012-09-01
卷号	55 期号:9 页码:1088-1102
文章类型	Article
摘要	In this paper, we propose an approach termed segment-based features (SBFs) to classify time series. The approach is inspired by the success of the component- or part-based methods of object recognition in computer vision, in which a visual object is described as a number of characteristic parts and the relations among the parts. Utilizing this idea in the problem of time series classification, a time series is represented as a set of segments and the corresponding temporal relations. First, a number of interest segments are extracted by interest point detection with automatic scale selection. Then, a number of feature prototypes are collected by random sampling from the segment set, where each feature prototype may include single segment or multiple ordered segments. Subsequently, each time series is transformed to a standard feature vector, i.e. SBF, where each entry in the SBF is calculated as the maximum response (maximum similarity) of the corresponding feature prototype to the segment set of the time series. Based on the original SBF, an incremental feature selection algorithm is conducted to form a compact and discriminative feature representation. Finally, a multi-class support vector machine is trained to classify the test time series. Extensive experiments on different time series datasets, including one synthetic control dataset, two sign language datasets and one gait dynamics dataset, have been performed to evaluate the proposed SBF method. Compared with other state-of-the-art methods, our approach achieves superior classification performance, which clearly validates the advantages of the proposed method.
关键词	Time Series Classification Segment-based Features Matching
WOS标题词	Science & Technology ; Technology
关键词[WOS]	RECOGNITION ; MODELS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000308233900006
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/7996
专题	模式识别实验室
作者单位	1.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Shenzhen, Peoples R China 3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China 4.Chinese Univ Hong Kong, Shatin, Hong Kong, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhang, Zhang,Cheng, Jun,Li, Jun,et al. Segment-Based Features for Time Series Classification[J]. COMPUTER JOURNAL,2012,55(9):1088-1102.
APA	Zhang, Zhang,Cheng, Jun,Li, Jun,Bian, Wei,&Tao, Dacheng.(2012).Segment-Based Features for Time Series Classification.COMPUTER JOURNAL,55(9),1088-1102.
MLA	Zhang, Zhang,et al."Segment-Based Features for Time Series Classification".COMPUTER JOURNAL 55.9(2012):1088-1102.