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

H. W. Cho, S. H. Baek, E. Youn, M. K. Jeong, A. Taylor

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.Journal of the Operational Research Society (2009) 60, 1107-1115. doi:10.1057/jors.2008. 179; published online 8 April 2009.

Original languageEnglish (US)
Pages (from-to)1107-1115
Number of pages9
JournalJournal of the Operational Research Society
Volume60
Issue number8
DOIs
StatePublished - Aug 2009

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Strategy and Management
  • Management Science and Operations Research
  • Marketing

Keywords

  • Classification
  • Feature selection
  • Spectra data
  • Support vector machines
  • Thresholding
  • Wavelet analysis

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