Shapelet Discovery by Lazy Time Series Classification

As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information con...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2020, Vol.2020 (2020), p.1-19
Main Authors: Hao, Shilei, Yuan, Jidong, Wang, Zhihai, Zhang, Wei
Format: Article
Language:English
Subjects:
Algorithms
Classification
Computer applications
Datasets
Decision trees
Evaluation
Feature extraction
Methods
Optimization
Time series
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Summary:As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and local similarities. In the prediction process, the strategy learns a specific evaluation dataset for each instance, and then the captured characteristics are directly used to progressively reduce the uncertainty of the predicted class label. Moreover, a shapelet coverage score is defined to calculate the discriminability of each time stamp for different classes. The experimental results show that the proposed method is competitive with the benchmark methods and provides insight into the discriminative features of each time series and each type in the data.
ISSN:1687-5265
1687-5273
DOI:10.1155/2020/1978310