Article ID: | iaor200969058 |
Country: | United Kingdom |
Volume: | 60 |
Issue: | 8 |
Start Page Number: | 1107 |
End Page Number: | 1115 |
Publication Date: | Aug 2009 |
Journal: | Journal of the Operational Research Society |
Authors: | Cho H-W, Baek S H, Youn E, Jeong M K, Taylor A |
Keywords: | classification, wavelets, spectroscopy |
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.