Automated adaptation strategies for stream learning
Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bakirov, Rashid [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Springer US, 1986, 110(2021), 6 vom: Juni, Seite 1429-1462 |
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| Übergeordnetes Werk: |
volume:110 ; year:2021 ; number:6 ; month:06 ; pages:1429-1462 |
| Links: |
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| DOI / URN: |
10.1007/s10994-021-05992-x |
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OLC2126148149 |
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| 520 | |a Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. | ||
| 650 | 4 | |a Adaptive machine learning | |
| 650 | 4 | |a Streaming data | |
| 650 | 4 | |a Non-stationary data | |
| 650 | 4 | |a Concept drift | |
| 650 | 4 | |a Automated machine learning | |
| 700 | 1 | |a Fay, Damien |4 aut | |
| 700 | 1 | |a Gabrys, Bogdan |4 aut | |
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10.1007/s10994-021-05992-x doi (DE-627)OLC2126148149 (DE-He213)s10994-021-05992-x-p DE-627 ger DE-627 rakwb eng 150 004 VZ Bakirov, Rashid verfasserin (orcid)0000-0002-2809-9626 aut Automated adaptation strategies for stream learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. Adaptive machine learning Streaming data Non-stationary data Concept drift Automated machine learning Fay, Damien aut Gabrys, Bogdan aut Enthalten in Machine learning Springer US, 1986 110(2021), 6 vom: Juni, Seite 1429-1462 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:110 year:2021 number:6 month:06 pages:1429-1462 https://doi.org/10.1007/s10994-021-05992-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 110 2021 6 06 1429-1462 |
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10.1007/s10994-021-05992-x doi (DE-627)OLC2126148149 (DE-He213)s10994-021-05992-x-p DE-627 ger DE-627 rakwb eng 150 004 VZ Bakirov, Rashid verfasserin (orcid)0000-0002-2809-9626 aut Automated adaptation strategies for stream learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. Adaptive machine learning Streaming data Non-stationary data Concept drift Automated machine learning Fay, Damien aut Gabrys, Bogdan aut Enthalten in Machine learning Springer US, 1986 110(2021), 6 vom: Juni, Seite 1429-1462 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:110 year:2021 number:6 month:06 pages:1429-1462 https://doi.org/10.1007/s10994-021-05992-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 110 2021 6 06 1429-1462 |
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10.1007/s10994-021-05992-x doi (DE-627)OLC2126148149 (DE-He213)s10994-021-05992-x-p DE-627 ger DE-627 rakwb eng 150 004 VZ Bakirov, Rashid verfasserin (orcid)0000-0002-2809-9626 aut Automated adaptation strategies for stream learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. Adaptive machine learning Streaming data Non-stationary data Concept drift Automated machine learning Fay, Damien aut Gabrys, Bogdan aut Enthalten in Machine learning Springer US, 1986 110(2021), 6 vom: Juni, Seite 1429-1462 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:110 year:2021 number:6 month:06 pages:1429-1462 https://doi.org/10.1007/s10994-021-05992-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 110 2021 6 06 1429-1462 |
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10.1007/s10994-021-05992-x doi (DE-627)OLC2126148149 (DE-He213)s10994-021-05992-x-p DE-627 ger DE-627 rakwb eng 150 004 VZ Bakirov, Rashid verfasserin (orcid)0000-0002-2809-9626 aut Automated adaptation strategies for stream learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. Adaptive machine learning Streaming data Non-stationary data Concept drift Automated machine learning Fay, Damien aut Gabrys, Bogdan aut Enthalten in Machine learning Springer US, 1986 110(2021), 6 vom: Juni, Seite 1429-1462 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:110 year:2021 number:6 month:06 pages:1429-1462 https://doi.org/10.1007/s10994-021-05992-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 110 2021 6 06 1429-1462 |
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10.1007/s10994-021-05992-x doi (DE-627)OLC2126148149 (DE-He213)s10994-021-05992-x-p DE-627 ger DE-627 rakwb eng 150 004 VZ Bakirov, Rashid verfasserin (orcid)0000-0002-2809-9626 aut Automated adaptation strategies for stream learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. Adaptive machine learning Streaming data Non-stationary data Concept drift Automated machine learning Fay, Damien aut Gabrys, Bogdan aut Enthalten in Machine learning Springer US, 1986 110(2021), 6 vom: Juni, Seite 1429-1462 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:110 year:2021 number:6 month:06 pages:1429-1462 https://doi.org/10.1007/s10994-021-05992-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 110 2021 6 06 1429-1462 |
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Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. © The Author(s) 2021 |
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Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. © The Author(s) 2021 |
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Abstract Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism. © The Author(s) 2021 |
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While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Streaming data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Non-stationary data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Concept drift</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automated machine learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fay, Damien</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gabrys, Bogdan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Machine learning</subfield><subfield code="d">Springer US, 1986</subfield><subfield code="g">110(2021), 6 vom: Juni, Seite 1429-1462</subfield><subfield code="w">(DE-627)12920403X</subfield><subfield code="w">(DE-600)54638-0</subfield><subfield code="w">(DE-576)014457377</subfield><subfield code="x">0885-6125</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:110</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:6</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:1429-1462</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10994-021-05992-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">110</subfield><subfield code="j">2021</subfield><subfield code="e">6</subfield><subfield code="c">06</subfield><subfield code="h">1429-1462</subfield></datafield></record></collection>
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