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

Cho H-W, S H Baek, E Youn, M K Jeong () and A Taylor
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Cho H-W: 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

Journal of the Operational Research Society, 2009, vol. 60, issue 8, 1107-1115

Abstract: 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.

Keywords: spectra data; classification; wavelet analysis; thresholding; support vector machines; feature selection (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1057/jors.2008.179

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