GENDIS: Genetic Discovery of Shapelets

@article{Vandewiele2019GENDISGD,
  title={GENDIS: Genetic Discovery of Shapelets},
  author={Gilles Vandewiele and Femke Ongenae and Filip De Turck},
  journal={Sensors (Basel, Switzerland)},
  year={2019},
  volume={21}
}
In the time series classification domain, shapelets are subsequences that are discriminative of a certain class. It has been shown that classifiers are able to achieve state-of-the-art results by taking the distances from the input time series to different discriminative shapelets as the input. Additionally, these shapelets can be visualized and thus possess an interpretable characteristic, making them appealing in critical domains, where longitudinal data are ubiquitous. In this study, a new… 
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6 Citations
Background Citations
1
Methods Citations
3

6 Citations

Convolutional Shapelet Transform: A new approach for time series shapelets

This work presents a new formulation of time series shapelets including the notion of dilation, and a shapelet extraction method based on convolutional kernels, which is able to target the discriminant informations identified by convolutionAL kernels.

Shapelet Selection for Efficient Time Series Classification by Dynamic Time Warping

The proposed shapelet selection method uses DTW(dynamic time warping) searches for frequent patterns occurring in time series through the warping path of DTW and uses it as shapelets and achieves excellent performance in classification accuracy.

Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets

A new formulation of time series shapelets including the notion of dilation is presented, and a new shapelet feature is introduced to enhance their discriminative power for classification.

A Customized Machine Learning Algorithm for Discovering the Shapes of Recovery: Was the Global Financial Crisis Different?

In this paper, we modify a conventional machine learning technique to classify recession-and-recovery events emerging in the countries’ business cycles. We do this by analyzing output dynamics in…

Fast Shapelet Learning for Power System Dominant Instability Mode Identification

A fast shapelet learning method to extract features from the original voltage and rotor angle curves and then classify them through a machine learning (ML) model is proposed to overcome the huge time complexity of the shapelet transformation method.

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

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