An Effective Lazy Shapelet Discovery Algorithm for Time Series Classification

Zhang, Wei; Wang, Zhihai; Yuan, Jiazheng; Hao, Shilei

doi:10.1007/978-3-030-04224-0_5

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…To address these problems, we have first proposed a lazy shapelet-based model to capture the local features of each instance in the literature [ 37 ] (the literature [ 37 ] is a poster in the conference ICONIP 2018. In the conference manuscript, we simply proposed a lazy model to classify the instance based on its own local features.…”

Section: Introductionmentioning

confidence: 99%

Shapelet Discovery by Lazy Time Series Classification

Zhang

Wang

Yuan

et al. 2020

Computational Intelligence and Neuroscience

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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.

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Section: Introductionmentioning

confidence: 99%

Shapelet Discovery by Lazy Time Series Classification

Zhang

Wang

Yuan

et al. 2020

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

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Discriminative Dictionary Learning for Time Series Classification

et al. 2020

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Time series symbolization based on the Symbolic Fourier Approximation (SFA) and a sliding window mechanism can effectively improve classification performance. Hence, it has become a research hotspot of time series representation learning. However, there are still some obvious shortcomings. First, the lengths of the generated words are consistent for different sliding windows in the symbolization process, ignoring the difference of the discriminative information contained in diverse periods. Second, research on the relationship between words is limited to adjacent ones. Third, existing dictionary learning methods do not consider a filtering algorithm for discriminative word selection. To this end, a novel and fast variable-length word generation method, incorporating the skip-bigram model for the construction of symbiotic word pairs, is proposed for the first time in our work. Then, a discriminative word filter with a dynamic threshold is designed to build the discriminative word dictionary. Many controlled experiments first verified the effectiveness of each proposed method. Then, the performance improvement ability of the word dictionary is demonstrated by the comparative experiments with the representative classification models based on different theoretical foundations.

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An Effective Lazy Shapelet Discovery Algorithm for Time Series Classification

Cited by 2 publications

References 13 publications

Shapelet Discovery by Lazy Time Series Classification

Shapelet Discovery by Lazy Time Series Classification

Discriminative Dictionary Learning for Time Series Classification

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