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Segment-Based Features for Time Series Classification
Zhang, Zhang2; Cheng, Jun3,4; Li, Jun1; Bian, Wei1; Tao, Dacheng1
Source PublicationCOMPUTER JOURNAL
AbstractIn 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.
KeywordTime Series Classification Segment-based Features Matching
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:000308233900006
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
Recommended Citation
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.
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