A combination strategy of random forest and back propagation network for variable selection in spectral calibration

Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be s...
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Autor*in:	Chen, Huazhou [verfasserIn] Liu, Xiaoke [verfasserIn] Jia, Zhen [verfasserIn] Liu, Zhenyao [verfasserIn] Shi, Kai [verfasserIn] Cai, Ken [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Variable selection Random forest Gini index Back propagation network Fourier transform near infrared spectroscopy Soil organic carbon

Übergeordnetes Werk:	Enthalten in: Chemometrics and intelligent laboratory systems - Amsterdam [u.a.] : Elsevier Science, 1986, 182, Seite 101-108
Übergeordnetes Werk:	volume:182 ; pages:101-108

DOI / URN:	10.1016/j.chemolab.2018.09.002

Katalog-ID:	ELV001009699

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245	1	0	\|a A combination strategy of random forest and back propagation network for variable selection in spectral calibration
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520			\|a Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture.
650		4	\|a Variable selection
650		4	\|a Random forest
650		4	\|a Gini index
650		4	\|a Back propagation network
650		4	\|a Fourier transform near infrared spectroscopy
650		4	\|a Soil organic carbon
700	1		\|a Liu, Xiaoke \|e verfasserin \|4 aut
700	1		\|a Jia, Zhen \|e verfasserin \|4 aut
700	1		\|a Liu, Zhenyao \|e verfasserin \|4 aut
700	1		\|a Shi, Kai \|e verfasserin \|4 aut
700	1		\|a Cai, Ken \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Chemometrics and intelligent laboratory systems \|d Amsterdam [u.a.] : Elsevier Science, 1986 \|g 182, Seite 101-108 \|h Online-Ressource \|w (DE-627)320603512 \|w (DE-600)2020467-X \|w (DE-576)255554133 \|x 0169-7439 \|7 nnns
773	1	8	\|g volume:182 \|g pages:101-108
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936	b	k	\|a 35.07 \|j Chemisches Labor \|j chemische Methoden
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matchkey_str	article:01697439:2018----::cmiaintaeyfadmoetnbcpoaainewrfraibeee
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publishDate	2018
allfields	10.1016/j.chemolab.2018.09.002 doi (DE-627)ELV001009699 (ELSEVIER)S0169-7439(18)30097-2 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Chen, Huazhou verfasserin aut A combination strategy of random forest and back propagation network for variable selection in spectral calibration 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture. Variable selection Random forest Gini index Back propagation network Fourier transform near infrared spectroscopy Soil organic carbon Liu, Xiaoke verfasserin aut Jia, Zhen verfasserin aut Liu, Zhenyao verfasserin aut Shi, Kai verfasserin aut Cai, Ken verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 182, Seite 101-108 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:182 pages:101-108 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 182 101-108
spelling	10.1016/j.chemolab.2018.09.002 doi (DE-627)ELV001009699 (ELSEVIER)S0169-7439(18)30097-2 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Chen, Huazhou verfasserin aut A combination strategy of random forest and back propagation network for variable selection in spectral calibration 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture. Variable selection Random forest Gini index Back propagation network Fourier transform near infrared spectroscopy Soil organic carbon Liu, Xiaoke verfasserin aut Jia, Zhen verfasserin aut Liu, Zhenyao verfasserin aut Shi, Kai verfasserin aut Cai, Ken verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 182, Seite 101-108 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:182 pages:101-108 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 182 101-108
allfields_unstemmed	10.1016/j.chemolab.2018.09.002 doi (DE-627)ELV001009699 (ELSEVIER)S0169-7439(18)30097-2 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Chen, Huazhou verfasserin aut A combination strategy of random forest and back propagation network for variable selection in spectral calibration 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture. Variable selection Random forest Gini index Back propagation network Fourier transform near infrared spectroscopy Soil organic carbon Liu, Xiaoke verfasserin aut Jia, Zhen verfasserin aut Liu, Zhenyao verfasserin aut Shi, Kai verfasserin aut Cai, Ken verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 182, Seite 101-108 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:182 pages:101-108 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 182 101-108
allfieldsGer	10.1016/j.chemolab.2018.09.002 doi (DE-627)ELV001009699 (ELSEVIER)S0169-7439(18)30097-2 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Chen, Huazhou verfasserin aut A combination strategy of random forest and back propagation network for variable selection in spectral calibration 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture. Variable selection Random forest Gini index Back propagation network Fourier transform near infrared spectroscopy Soil organic carbon Liu, Xiaoke verfasserin aut Jia, Zhen verfasserin aut Liu, Zhenyao verfasserin aut Shi, Kai verfasserin aut Cai, Ken verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 182, Seite 101-108 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:182 pages:101-108 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 182 101-108
allfieldsSound	10.1016/j.chemolab.2018.09.002 doi (DE-627)ELV001009699 (ELSEVIER)S0169-7439(18)30097-2 DE-627 ger DE-627 rda eng 540 DE-600 35.07 bkl 35.05 bkl Chen, Huazhou verfasserin aut A combination strategy of random forest and back propagation network for variable selection in spectral calibration 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture. Variable selection Random forest Gini index Back propagation network Fourier transform near infrared spectroscopy Soil organic carbon Liu, Xiaoke verfasserin aut Jia, Zhen verfasserin aut Liu, Zhenyao verfasserin aut Shi, Kai verfasserin aut Cai, Ken verfasserin aut Enthalten in Chemometrics and intelligent laboratory systems Amsterdam [u.a.] : Elsevier Science, 1986 182, Seite 101-108 Online-Ressource (DE-627)320603512 (DE-600)2020467-X (DE-576)255554133 0169-7439 nnns volume:182 pages:101-108 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.07 Chemisches Labor chemische Methoden 35.05 Mathematische Chemie chemische Statistik AR 182 101-108
language	English
source	Enthalten in Chemometrics and intelligent laboratory systems 182, Seite 101-108 volume:182 pages:101-108
sourceStr	Enthalten in Chemometrics and intelligent laboratory systems 182, Seite 101-108 volume:182 pages:101-108
format_phy_str_mv	Article
bklname	Chemisches Labor chemische Methoden Mathematische Chemie chemische Statistik
institution	findex.gbv.de
topic_facet	Variable selection Random forest Gini index Back propagation network Fourier transform near infrared spectroscopy Soil organic carbon
dewey-raw	540
isfreeaccess_bool	false
container_title	Chemometrics and intelligent laboratory systems
authorswithroles_txt_mv	Chen, Huazhou @@aut@@ Liu, Xiaoke @@aut@@ Jia, Zhen @@aut@@ Liu, Zhenyao @@aut@@ Shi, Kai @@aut@@ Cai, Ken @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	320603512
dewey-sort	3540
id	ELV001009699
language_de	englisch
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author	Chen, Huazhou
spellingShingle	Chen, Huazhou ddc 540 bkl 35.07 bkl 35.05 misc Variable selection misc Random forest misc Gini index misc Back propagation network misc Fourier transform near infrared spectroscopy misc Soil organic carbon A combination strategy of random forest and back propagation network for variable selection in spectral calibration
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topic_title	540 DE-600 35.07 bkl 35.05 bkl A combination strategy of random forest and back propagation network for variable selection in spectral calibration Variable selection Random forest Gini index Back propagation network Fourier transform near infrared spectroscopy Soil organic carbon
topic	ddc 540 bkl 35.07 bkl 35.05 misc Variable selection misc Random forest misc Gini index misc Back propagation network misc Fourier transform near infrared spectroscopy misc Soil organic carbon
topic_unstemmed	ddc 540 bkl 35.07 bkl 35.05 misc Variable selection misc Random forest misc Gini index misc Back propagation network misc Fourier transform near infrared spectroscopy misc Soil organic carbon
topic_browse	ddc 540 bkl 35.07 bkl 35.05 misc Variable selection misc Random forest misc Gini index misc Back propagation network misc Fourier transform near infrared spectroscopy misc Soil organic carbon
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title	A combination strategy of random forest and back propagation network for variable selection in spectral calibration
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title_full	A combination strategy of random forest and back propagation network for variable selection in spectral calibration
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author_browse	Chen, Huazhou Liu, Xiaoke Jia, Zhen Liu, Zhenyao Shi, Kai Cai, Ken
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title_sort	a combination strategy of random forest and back propagation network for variable selection in spectral calibration
title_auth	A combination strategy of random forest and back propagation network for variable selection in spectral calibration
abstract	Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture.
abstractGer	Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture.
abstract_unstemmed	Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture.
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title_short	A combination strategy of random forest and back propagation network for variable selection in spectral calibration
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author2	Liu, Xiaoke Jia, Zhen Liu, Zhenyao Shi, Kai Cai, Ken
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up_date	2024-07-06T19:55:14.602Z
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A combination strategy of random forest and back propagation network for variable selection in spectral calibration

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