Application and comparison of several machine learning algorithms and their integration models in regression problems
Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, th...
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| Autor*in: |
Huang, Jui-Chan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 32(2019), 10 vom: 30. Nov., Seite 5461-5469 |
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| Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:10 ; day:30 ; month:11 ; pages:5461-5469 |
| Links: |
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| DOI / URN: |
10.1007/s00521-019-04644-5 |
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OLC2025620322 |
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| 520 | |a Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. | ||
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10.1007/s00521-019-04644-5 doi (DE-627)OLC2025620322 (DE-He213)s00521-019-04644-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Jui-Chan verfasserin aut Application and comparison of several machine learning algorithms and their integration models in regression problems 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. Machine learning Regression problem BP neural network Extreme learning machine Support vector machine Ko, Kuo-Min aut Shu, Ming-Hung aut Hsu, Bi-Min aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 10 vom: 30. Nov., Seite 5461-5469 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:10 day:30 month:11 pages:5461-5469 https://doi.org/10.1007/s00521-019-04644-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 10 30 11 5461-5469 |
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10.1007/s00521-019-04644-5 doi (DE-627)OLC2025620322 (DE-He213)s00521-019-04644-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Jui-Chan verfasserin aut Application and comparison of several machine learning algorithms and their integration models in regression problems 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. Machine learning Regression problem BP neural network Extreme learning machine Support vector machine Ko, Kuo-Min aut Shu, Ming-Hung aut Hsu, Bi-Min aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 10 vom: 30. Nov., Seite 5461-5469 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:10 day:30 month:11 pages:5461-5469 https://doi.org/10.1007/s00521-019-04644-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 10 30 11 5461-5469 |
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10.1007/s00521-019-04644-5 doi (DE-627)OLC2025620322 (DE-He213)s00521-019-04644-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Jui-Chan verfasserin aut Application and comparison of several machine learning algorithms and their integration models in regression problems 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. Machine learning Regression problem BP neural network Extreme learning machine Support vector machine Ko, Kuo-Min aut Shu, Ming-Hung aut Hsu, Bi-Min aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 10 vom: 30. Nov., Seite 5461-5469 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:10 day:30 month:11 pages:5461-5469 https://doi.org/10.1007/s00521-019-04644-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 10 30 11 5461-5469 |
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10.1007/s00521-019-04644-5 doi (DE-627)OLC2025620322 (DE-He213)s00521-019-04644-5-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Jui-Chan verfasserin aut Application and comparison of several machine learning algorithms and their integration models in regression problems 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. Machine learning Regression problem BP neural network Extreme learning machine Support vector machine Ko, Kuo-Min aut Shu, Ming-Hung aut Hsu, Bi-Min aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 10 vom: 30. Nov., Seite 5461-5469 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:10 day:30 month:11 pages:5461-5469 https://doi.org/10.1007/s00521-019-04644-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 10 30 11 5461-5469 |
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Application and comparison of several machine learning algorithms and their integration models in regression problems |
| abstract |
Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstractGer |
Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
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| title_short |
Application and comparison of several machine learning algorithms and their integration models in regression problems |
| url |
https://doi.org/10.1007/s00521-019-04644-5 |
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| author2 |
Ko, Kuo-Min Shu, Ming-Hung Hsu, Bi-Min |
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Ko, Kuo-Min Shu, Ming-Hung Hsu, Bi-Min |
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| doi_str |
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2024-07-04T01:43:54.933Z |
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