A two-stage classification procedure for near-infrared spectra based on multi-scale vertical energy wavelet thresholding and SVM-based gradient-recursive feature elimination
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Hyunwoo | - |
dc.contributor.author | Baek, Seung-hyun | - |
dc.contributor.author | Youn, Eunseog | - |
dc.contributor.author | Jeong, Myongkee | - |
dc.contributor.author | Taylor, Adam M. | - |
dc.date.accessioned | 2021-06-23T15:06:03Z | - |
dc.date.available | 2021-06-23T15:06:03Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2009-08 | - |
dc.identifier.issn | 0160-5682 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/40996 | - |
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. Journal of the Operational Research Society (2009) 60, 1107-1115. doi:10.1057/jors.2008.179 Published online 8 April 2009 | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Palgrave Macmillan Ltd. | - |
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 | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Baek, Seung-hyun | - |
dc.identifier.doi | 10.1057/jors.2008.179 | - |
dc.identifier.scopusid | 2-s2.0-68149158363 | - |
dc.identifier.wosid | 000267603300007 | - |
dc.identifier.bibliographicCitation | Journal of the Operational Research Society, v.60, no.8, pp.1107 - 1115 | - |
dc.relation.isPartOf | Journal of the Operational Research Society | - |
dc.citation.title | Journal of the Operational Research Society | - |
dc.citation.volume | 60 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 1107 | - |
dc.citation.endPage | 1115 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Business & Economics | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Management | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | SUPPORT VECTOR MACHINES | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | DATA REDUCTION | - |
dc.subject.keywordPlus | COMPRESSION | - |
dc.subject.keywordPlus | SHRINKAGE | - |
dc.subject.keywordAuthor | spectra data | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | wavelet analysis | - |
dc.subject.keywordAuthor | thresholding | - |
dc.subject.keywordAuthor | support vector machines | - |
dc.subject.keywordAuthor | feature selection | - |
dc.identifier.url | https://www.tandfonline.com/doi/full/10.1057/jors.2008.179 | - |
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