GENDIS: Genetic Discovery of Shapelets
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. Addi...
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doaj-0f8d9f2d62c6410899484175fdf084af2021-02-05T00:02:48ZengMDPI AGSensors1424-82202021-02-01211059105910.3390/s21041059GENDIS: Genetic Discovery of ShapeletsGilles Vandewiele0Femke Ongenae1Filip De Turck2IDLab, Ghent University—imec, 9052 Ghent, BelgiumIDLab, Ghent University—imec, 9052 Ghent, BelgiumIDLab, Ghent University—imec, 9052 Ghent, BelgiumIn 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 paradigm for shapelet discovery is proposed, which is based on evolutionary computation. The advantages of the proposed approach are that: (i) it is gradient-free, which could allow escaping from local optima more easily and supports non-differentiable objectives; (ii) no brute-force search is required, making the algorithm scalable; (iii) the total amount of shapelets and the length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand; (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with fewer similar shapelets that result in similar predictive performances; and (v) the discovered shapelets do not need to be a subsequence of the input time series. We present the results of the experiments, which validate the enumerated advantages.https://www.mdpi.com/1424-8220/21/4/1059genetic algorithmstime series classificationtime series analysisexplainable artificial intelligence (xAI)data mining |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gilles Vandewiele Femke Ongenae Filip De Turck |
spellingShingle |
Gilles Vandewiele Femke Ongenae Filip De Turck GENDIS: Genetic Discovery of Shapelets Sensors genetic algorithms time series classification time series analysis explainable artificial intelligence (xAI) data mining |
author_facet |
Gilles Vandewiele Femke Ongenae Filip De Turck |
author_sort |
Gilles Vandewiele |
title |
GENDIS: Genetic Discovery of Shapelets |
title_short |
GENDIS: Genetic Discovery of Shapelets |
title_full |
GENDIS: Genetic Discovery of Shapelets |
title_fullStr |
GENDIS: Genetic Discovery of Shapelets |
title_full_unstemmed |
GENDIS: Genetic Discovery of Shapelets |
title_sort |
gendis: genetic discovery of shapelets |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-02-01 |
description |
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 paradigm for shapelet discovery is proposed, which is based on evolutionary computation. The advantages of the proposed approach are that: (i) it is gradient-free, which could allow escaping from local optima more easily and supports non-differentiable objectives; (ii) no brute-force search is required, making the algorithm scalable; (iii) the total amount of shapelets and the length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand; (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with fewer similar shapelets that result in similar predictive performances; and (v) the discovered shapelets do not need to be a subsequence of the input time series. We present the results of the experiments, which validate the enumerated advantages. |
topic |
genetic algorithms time series classification time series analysis explainable artificial intelligence (xAI) data mining |
url |
https://www.mdpi.com/1424-8220/21/4/1059 |
work_keys_str_mv |
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