A two-phase filtering of discriminative shapelets learning for time series classification
Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidat...
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| Autor*in: |
Li, Chen [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 53(2022), 11 vom: 17. Okt., Seite 13815-13833 |
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| Übergeordnetes Werk: |
volume:53 ; year:2022 ; number:11 ; day:17 ; month:10 ; pages:13815-13833 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10489-022-04043-9 |
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OLC2143603231 |
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| 520 | |a Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. | ||
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| 700 | 1 | |a Zhang, Wenjing |4 aut | |
| 700 | 1 | |a Li, Huanhuan |4 aut | |
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10.1007/s10489-022-04043-9 doi (DE-627)OLC2143603231 (DE-He213)s10489-022-04043-9-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Chen verfasserin aut A two-phase filtering of discriminative shapelets learning for time series classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. Sparse group lasso Shapelets Extreme key points Group sparsity degree Wan, Yuan aut Zhang, Wenjing aut Li, Huanhuan aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 11 vom: 17. Okt., Seite 13815-13833 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:11 day:17 month:10 pages:13815-13833 https://doi.org/10.1007/s10489-022-04043-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 11 17 10 13815-13833 |
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10.1007/s10489-022-04043-9 doi (DE-627)OLC2143603231 (DE-He213)s10489-022-04043-9-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Chen verfasserin aut A two-phase filtering of discriminative shapelets learning for time series classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. Sparse group lasso Shapelets Extreme key points Group sparsity degree Wan, Yuan aut Zhang, Wenjing aut Li, Huanhuan aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 11 vom: 17. Okt., Seite 13815-13833 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:11 day:17 month:10 pages:13815-13833 https://doi.org/10.1007/s10489-022-04043-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 11 17 10 13815-13833 |
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10.1007/s10489-022-04043-9 doi (DE-627)OLC2143603231 (DE-He213)s10489-022-04043-9-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Chen verfasserin aut A two-phase filtering of discriminative shapelets learning for time series classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. Sparse group lasso Shapelets Extreme key points Group sparsity degree Wan, Yuan aut Zhang, Wenjing aut Li, Huanhuan aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 11 vom: 17. Okt., Seite 13815-13833 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:11 day:17 month:10 pages:13815-13833 https://doi.org/10.1007/s10489-022-04043-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 11 17 10 13815-13833 |
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10.1007/s10489-022-04043-9 doi (DE-627)OLC2143603231 (DE-He213)s10489-022-04043-9-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Chen verfasserin aut A two-phase filtering of discriminative shapelets learning for time series classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. Sparse group lasso Shapelets Extreme key points Group sparsity degree Wan, Yuan aut Zhang, Wenjing aut Li, Huanhuan aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 11 vom: 17. Okt., Seite 13815-13833 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:11 day:17 month:10 pages:13815-13833 https://doi.org/10.1007/s10489-022-04043-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 11 17 10 13815-13833 |
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10.1007/s10489-022-04043-9 doi (DE-627)OLC2143603231 (DE-He213)s10489-022-04043-9-p DE-627 ger DE-627 rakwb eng 004 VZ Li, Chen verfasserin aut A two-phase filtering of discriminative shapelets learning for time series classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. Sparse group lasso Shapelets Extreme key points Group sparsity degree Wan, Yuan aut Zhang, Wenjing aut Li, Huanhuan aut Enthalten in Applied intelligence Springer US, 1991 53(2022), 11 vom: 17. Okt., Seite 13815-13833 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:53 year:2022 number:11 day:17 month:10 pages:13815-13833 https://doi.org/10.1007/s10489-022-04043-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 53 2022 11 17 10 13815-13833 |
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A two-phase filtering of discriminative shapelets learning for time series classification |
| abstract |
Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Compared to the full-length methods for time series classification, shapelet-based methods acquire better interpretation, higher efficiency and precision since shapelets are discriminative features that well represent a time series. However, because of the large number of shapelets candidates, determining how to filter out shapelets with higher discriminability remains a challenge. In this paper, we propose a two-phase shapelets learning filtering framework for time series classification. Time series is first split into groups using the extreme key points, and local linear discriminant analysis with sparse group lasso regularizer is proposed to find projection vector. Then, a two-phase filtering framework is established to measure the sparsity of groups in order to quickly find the key group, where l2-norm is introduced in phase-1 and group sparsity degree is defined in phase-2 to filter sparse groups. Following that, only a few groups are used to extract shapelets and classify them, reducing the number of shapelets significantly. Finally, the group with the highest classification accuracy, i.e., the key group, is determined accurately. Extensive experiments on 28 time series datasets show that, when compared to other state-of-the-art shapelet-based classification methods, our proposed method achieves significant improvement and a competitive time cost. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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A two-phase filtering of discriminative shapelets learning for time series classification |
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https://doi.org/10.1007/s10489-022-04043-9 |
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| author2 |
Wan, Yuan Zhang, Wenjing Li, Huanhuan |
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Wan, Yuan Zhang, Wenjing Li, Huanhuan |
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| doi_str |
10.1007/s10489-022-04043-9 |
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2024-07-03T16:54:09.427Z |
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7.402793 |