| 光谱学与光谱分析 |
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| Spectrum Quantitative Analysis Based on Bootstrap-SVM Model with Small Sample Set |
| MA Xiao, ZHAO Zhong*, XIONG Shan-hai |
| College of Information Science and Technology, Bejing University of Chemical Technology, Beijing 100029, China |
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Abstract A new spectrum quantitative analysis method based on Bootstrap-SVM model with small sample set is proposed in this paper. To build the spectrum quantitative analysis model for bitumen penetration index, altogether 29 bitumen samples were collected from 6 companies. Based on the collected 29 bitumen samples, spectrum quantitative analysis model with proposed method for predicting bitumen penetration index has been built. To verify the feasibility and effectiveness of the proposed method, the comparative experiments of predicting the bitumen sample penetration index with the proposed method, partial least squares (PLS) and support vector machine (SVM) have also been done. Comparative experiment results have verified that the minimum prediction root mean squared error (RMSE) is achieved by using the proposed Bootstrap-SVM model with the small sample set. The proposed method provides a new way to solve the problem of building the spectrum quantitative analysis model with small sample set.
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Received: 2015-03-02
Accepted: 2015-07-09
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Corresponding Authors:
ZHAO Zhong
E-mail: zhaozhong@mail.buct.edu.cn
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[1] BIAN Zhao-qi，ZHANG Xue-gong. Pattern Recognition. Beijing: Tsinghua University Publishing Company, 2000. 192.
[2] Luo Wentao, Liu Guili. Modern Scientific Instruments, 2013, 6(3): 94.
[3] Roggo Y, Roeseler C, Ulmschneider M. J. Pharm. Biomed. Anal., 2004, 36(4): 777.
[4] Fontalvo-Gomez M, Colucci J A, Velez Natasha, Romanach R J. Applied Spectroscopy, 2013, 67(10): 1142.
[5] Mao R, Zhu H, Zhang L. A. Chen. Proc. ISDA, 2006, (1): 17.
[6] Lanouette R, Thibault J, Valade J L. Comput. Chem. Eng.，1999, 23(9): 1167.
[7] Luigi Fortuna, Salvatore Graziani, Maria Gabriella Xibilia. IEEE Transaction on Instrumentation and Measurement, 2009, 58(8): 2444.
[8] Efron B. The Annals and Statistics，1979, 7(1): 1.
[9] Grandvalet Y, Boucheron S. Neural Comput.，1997, 9(5): 1093. |
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