A review of improved extreme learning machine methods for data stream classification
Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspir...
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Gespeichert in:
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Li, Li [verfasserIn] |
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| Sprache: |
Englisch |
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2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Enthalten in: Multimedia tools and applications - Springer US, 1995, 78(2019), 23 vom: 25. Apr., Seite 33375-33400 |
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volume:78 ; year:2019 ; number:23 ; day:25 ; month:04 ; pages:33375-33400 |
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10.1007/s11042-019-7543-2 |
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| 520 | |a Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future. | ||
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| 700 | 1 | |a Zhang, Qianqian |4 aut | |
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10.1007/s11042-019-7543-2 doi (DE-627)OLC203507200X (DE-He213)s11042-019-7543-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Li, Li verfasserin aut A review of improved extreme learning machine methods for data stream classification 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future. Data streams Classification Improved extreme leaning machine Concept drifts Imbalanced data streams Uncertain data streams Sun, Ruizhi aut Cai, Saihua aut Zhao, Kaiyi aut Zhang, Qianqian aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2019), 23 vom: 25. Apr., Seite 33375-33400 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2019 number:23 day:25 month:04 pages:33375-33400 https://doi.org/10.1007/s11042-019-7543-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2019 23 25 04 33375-33400 |
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10.1007/s11042-019-7543-2 doi (DE-627)OLC203507200X (DE-He213)s11042-019-7543-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Li, Li verfasserin aut A review of improved extreme learning machine methods for data stream classification 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future. Data streams Classification Improved extreme leaning machine Concept drifts Imbalanced data streams Uncertain data streams Sun, Ruizhi aut Cai, Saihua aut Zhao, Kaiyi aut Zhang, Qianqian aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2019), 23 vom: 25. Apr., Seite 33375-33400 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2019 number:23 day:25 month:04 pages:33375-33400 https://doi.org/10.1007/s11042-019-7543-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2019 23 25 04 33375-33400 |
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10.1007/s11042-019-7543-2 doi (DE-627)OLC203507200X (DE-He213)s11042-019-7543-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Li, Li verfasserin aut A review of improved extreme learning machine methods for data stream classification 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future. Data streams Classification Improved extreme leaning machine Concept drifts Imbalanced data streams Uncertain data streams Sun, Ruizhi aut Cai, Saihua aut Zhao, Kaiyi aut Zhang, Qianqian aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2019), 23 vom: 25. Apr., Seite 33375-33400 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2019 number:23 day:25 month:04 pages:33375-33400 https://doi.org/10.1007/s11042-019-7543-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2019 23 25 04 33375-33400 |
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10.1007/s11042-019-7543-2 doi (DE-627)OLC203507200X (DE-He213)s11042-019-7543-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Li, Li verfasserin aut A review of improved extreme learning machine methods for data stream classification 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future. Data streams Classification Improved extreme leaning machine Concept drifts Imbalanced data streams Uncertain data streams Sun, Ruizhi aut Cai, Saihua aut Zhao, Kaiyi aut Zhang, Qianqian aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2019), 23 vom: 25. Apr., Seite 33375-33400 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2019 number:23 day:25 month:04 pages:33375-33400 https://doi.org/10.1007/s11042-019-7543-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2019 23 25 04 33375-33400 |
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Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Abstract Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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A review of improved extreme learning machine methods for data stream classification |
| url |
https://doi.org/10.1007/s11042-019-7543-2 |
| remote_bool |
false |
| author2 |
Sun, Ruizhi Cai, Saihua Zhao, Kaiyi Zhang, Qianqian |
| author2Str |
Sun, Ruizhi Cai, Saihua Zhao, Kaiyi Zhang, Qianqian |
| ppnlink |
189064145 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11042-019-7543-2 |
| up_date |
2024-07-03T23:42:03.217Z |
| _version_ |
1803603278004486144 |
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7.398222 |