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A two-stage classification procedure for near-infrared spectra based on multi-scale vertical energy wavelet thresholding and SVM-based gradient-recursive feature elimination

Author

Listed:
  • H-W Cho

    (University of Tennessee)

  • S H Baek

    (University of Tennessee)

  • E Youn

    (Texas Tech University)

  • M K Jeong

    (The State University of New Jersey)

  • A Taylor

    (University of Tennessee)

Abstract

Near infrared (NIR) spectroscopy has been extensively used in classification problems because it is fast, reliable, cost-effective, and non-destructive. However, NIR data often have several hundred or thousand variables (wavelengths) that are highly correlated with each other. Thus, it is critical to select a few important features or wavelengths that better explain NIR data. Wavelets are popular as preprocessing tools for spectra data. Many applications perform feature selection directly, based on high-dimensional wavelet coefficients, and this can be computationally expensive. This paper proposes a two-stage scheme for the classification of NIR spectra data. In the first stage, the proposed multi-scale vertical energy thresholding procedure is used to reduce the dimension of the high-dimensional spectral data. In the second stage, a few important wavelet coefficients are selected using the proposed support vector machines gradient-recursive feature elimination. The proposed two-stage method has produced better classification performance, with higher computational efficiency, when tested on four NIR data sets.

Suggested Citation

  • H-W Cho & S H Baek & E Youn & M K Jeong & A Taylor, 2009. "A two-stage classification procedure for near-infrared spectra based on multi-scale vertical energy wavelet thresholding and SVM-based gradient-recursive feature elimination," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1107-1115, August.
  • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:8:d:10.1057_jors.2008.179
    DOI: 10.1057/jors.2008.179
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    References listed on IDEAS

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    1. P. S. Bradley & O. L. Mangasarian & W. N. Street, 1998. "Feature Selection via Mathematical Programming," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 209-217, May.
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