Random Pairwise Shapelets Forest

“…Karlsson et al [ 31 ] constructed the shapelet-based random forest, in which each decision tree is built based on instances and shapelets selected randomly. Further, to omit the shapelet threshold search, Shi et al [ 32 ] have put forward the random pairwise shapelet forest. (4) Reformulating the shapelet search problem into a numerical optimization problem.…”

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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