A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis

Journal of Food Engineering

Volumn 119, Issue 1, 2013, Pages 159-166

  Deng, Shuiguang ^a,c   Xu, Yifei ^a   Li, Li ^a   Li, Xiaoli ^b,c   He, Yong ^b,c  

^a ZHEJIANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c ZHEJIANG UNIVERSITY   (China)

Author keywords

Feature selection; Feature Selection (SVM MFFS); Food quality inspection; Hyperspectral visible and near infrared (Vis NIR); Sesame oil; Support Vector Machine Multiclass Forward

Indexed keywords

FEATURE SELECTION ALGORITHM; FOOD QUALITY; FORWARD FEATURE SELECTIONS; RECEIVER OPERATING CHARACTERISTIC CURVES; SESAME OIL; SUPPORT VECTOR; UNINFORMATIVE VARIABLE ELIMINATIONS; VISIBLE AND NEAR INFRARED;

ALGORITHMS; FEATURE EXTRACTION; FOOD SAFETY; REGRESSION ANALYSIS; SPECTRUM ANALYSIS; VEGETABLE OILS;

SUPPORT VECTOR MACHINES;

SESAMUM INDICUM;

EID: 84879118238     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2013.05.024     Document Type: Article

References (27)

1. H. Abdi, and L.J. Williams Principal component analysis Wiley Interdisciplinary Reviews: Computational Statistics 2 4 2010 433 459
2. M.C.U. Araújo, T.C.B. Saldanha, R.K.H. Galvão, T. Yoneyama, H.C. Chame, and V. Visani The successive projections algorithm for variable selection in spectroscopic multicomponent analysis Chemometrics and Intelligent Laboratory Systems 57 2 2001 65 73
3. A.L. Blum, and P. Langley Selection of relevant features and examples in machine learning Artificial Intelligence 97 1-2 1997 245 271
4. V. Centner, D.-L. Massart, O.E. de Noord, S. de Jong, B.M. Vandeginste, and C. Sterna Elimination of uninformative variables for multivariate calibration Analytical Chemistry 68 21 1996 3851 3858
5. C.-C. Chang, and C.-J. Lin LIBSVM: a library for support vector machines ACM Transactions on Intelligence System and Technology 2 3 2011 1 27
6. H. Chih-Wei, and L. Chih-Jen A comparison of methods for multiclass support vector machines IEEE Transactions on Neural Networks 13 2 2002 415 425
7. Y.L. Cun, J.S. Denker, and S.A. Solla Optimal brain damage S.T. David, Advances in Neural Information Processing Systems 2 1990 Morgan Kaufmann Publishers Inc. 598 605
8. M. Darvin, I. Gersonde, S. Ey, N. Brandt, H. Albrecht, S. Gonchukov, W. Sterry, and J. Lademann Noninvasive detection of beta-carotene and lycopene in human skin using Raman spectroscopy LASER PHYSICS-LAWRENCE 14 2 2004 231 233
9. I. Guyon, and Elisseeff André An introduction to variable and feature selection Journal of Machine Learning Research 3 2003 1157 1182
10. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Machine Learning 46 1-3 2002 389 422
11. Y. He, M. Huang, and A. GARCIA Prediction of soil macronutrients content using near-infrared spectroscopy Computers and Electronics in Agriculture 58 2 2007 144 153
12. J. Heo, M.E. Troyer, S. Pattnaik, and L. Enkhbaatar A hierarchical approach to Compact Airborne Spectrographic Imager (CASI) high-resolution image classification of Little Miami River Watershed for environmental modelling International Journal of Remote Sensing 33 5 2011 1567 1585
13. A.K. Jain, R.P.W. Duin, and M. Jianchang Statistical pattern recognition: a review IEEE Transactions on Pattern Analysis and Machine Intelligence 22 1 2000 4 37
14. R.W. Kennard, and L.A. Stone Computer aided design of experiments Technometrics 11 1 1969 137 148
15. M. Kim, Y.-H. Lee, and C. Han Real-time classification of petroleum products using near-infrared spectra Computers Chemical Engineering 24 2-7 2000 513 517
16. R. Kohavi, and G.H. John Wrappers for feature subset selection Artificial Intelligence 97 1-2 1997 273 324
17. X. Li, Y. He, C. Wu, and D.-W. Sun Nondestructive measurement and fingerprint analysis of soluble solid content of tea soft drink based on Vis/NIR spectroscopy Journal of Food Engineering 82 3 2007 316 323
18. F. Lu, and Y. He Classification of brands of instant noodles using Vis/NIR spectroscopy and chemometrics Food Research International 41 5 2008 562 567
19. F. Lu, Y.H. Jiang, and Y. He Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: a case study to determine soluble solids content of beer Analytica Chimica Acta 635 1 2009 45 52
20. Z. Sun, G. Bebis, and R. Miller Object detection using feature subset selection Pattern Recognition 37 11 2004 2165 2176
21. V.N. Vapnik An overview of statistical learning theory IEEE Transactions on Neural Networks 10 5 1999 988 999
22. J. Wang, K. Nakano, S. Ohashi, K. Takizawa, and J.G. He Comparison of different modes of visible and near-infrared spectroscopy for detecting internal insect infestation in jujubes Journal of Food Engineering 101 1 2010 78 84
23. M. Wolf, M. Ferrari, and V. Quaresima Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications Journal of Biomedical Optics 12 6 2007 062104
24. D. Wu, H.Q. Yang, X.J. Chen, Y. He, and X.L. Li Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine Journal of Food Engineering 88 4 2008 474 483
25. X. Zhou, and D.P. Tuck MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data Bioinformatics 23 9 2007 1106 1114
26. X. Zhu, S. Li, Y. Shan, Z. Zhang, G. Li, D. Su, and F. Liu Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics Journal of Food Engineering 101 1 2010 92 97
27. M.H. Zweig, and G. Campbell Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine Clinical Chemistry 39 4 1993 561 577