Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 백승현 | - |
dc.date.accessioned | 2021-11-30T00:38:47Z | - |
dc.date.available | 2021-11-30T00:38:47Z | - |
dc.date.issued | 2009-04 | - |
dc.identifier.citation | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, v. 60, Issue. 8, Page. 1107-1115 | en_US |
dc.identifier.issn | 0160-5682 | - |
dc.identifier.issn | 1476-9360 | - |
dc.identifier.uri | https://www.proquest.com/docview/231383282?accountid=11283 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/166482 | - |
dc.description.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. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | PALGRAVE MACMILLAN LTD | en_US |
dc.subject | spectra data | en_US |
dc.subject | classification | en_US |
dc.subject | wavelet analysis | en_US |
dc.subject | thresholding | en_US |
dc.subject | support vector machines | en_US |
dc.subject | feature selection | en_US |
dc.title | A Two-Stage Classification Procedure for Near-Infrared Spectra Based on Multi-Scale Vertical Energy Wavelet Thresholding and SVM-Based Gradient-Recursive Feature Elimination | en_US |
dc.type | Article | en_US |
dc.relation.no | 8 | - |
dc.relation.volume | 60 | - |
dc.identifier.doi | 10.1057/jors.2008.179 | - |
dc.relation.page | 1107-1115 | - |
dc.relation.journal | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY | - |
dc.contributor.googleauthor | Cho, H.-W. | - |
dc.contributor.googleauthor | Youn, E. | - |
dc.contributor.googleauthor | Jeong, M. K. | - |
dc.contributor.googleauthor | Taylor, A. | - |
dc.contributor.googleauthor | Baek, S. H. | - |
dc.relation.code | 2012220492 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF BUSINESS AND ECONOMICS[E] | - |
dc.sector.department | DIVISION OF BUSINESS ADMINISTRATION | - |
dc.identifier.pid | sbaek4 | - |
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