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Adversarial Regularization for Explainable-by-Design Time Series Classification

Abstract : Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation. This shapelet-based classification is both accurate and explainable since the shapelets are time series themselves and thus can be visualized and be provided as a classification explanation. In this paper, we claim that not all shapelets are good visual explanations and we propose a simple, yet also accurate, adversarily regularized EXplainable Convolutional Neural Network, XCNN, that can learn shapelets that are, by design, suited for explanations. We validate our method on the usual univariate time series benchmarks of the UCR repository.
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Contributor : Elisa Fromont Connect in order to contact the contributor
Submitted on : Thursday, November 26, 2020 - 1:13:00 PM
Last modification on : Tuesday, October 25, 2022 - 4:23:04 PM
Long-term archiving on: : Saturday, February 27, 2021 - 7:07:51 PM


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  • HAL Id : hal-03025671, version 1


Yichang Wang, Rémi Emonet, Elisa Fromont, Simon Malinowski, Romain Tavenard. Adversarial Regularization for Explainable-by-Design Time Series Classification. ICTAI 2020 - 32th International Conference on Tools with Artificial Intelligence, Nov 2020, online, Greece. pp.1-9. ⟨hal-03025671⟩



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