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Article Dans Une Revue Pattern Recognition Letters Année : 2016

A spectral envelope approach towards effective SVM-RFE on infrared data

Une approche enveloppe spectrale pour améliorer l'algorithme SVM-RFE sur des données infra rouge

Résumé

Infrared spectroscopy data is characterized by the presence of a huge number of variables. Applications of infrared spectroscopy in the mid-infrared (MIR) and near-infrared (NIR) bands are of widespread use in many fields. To effectively handle this type of data, suitable dimensionality reduction methods are required. In this paper, a dimensionality reduction method designed to enable effective Support Vector Machine Recursive Feature Elimination (SVM-RFE) on NIR/MIR datasets is presented. The method exploits the information content at peaks of the spectral envelope functions which characterize NIR/MIR spectra datasets. Experimental evaluation across different NIR/MIR application domains shows that the proposed method is useful for the induction of compact and accurate SVM classifiers for qualitative NIR/MIR applications involving stringent interpretability or time processing requirements.
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Dates et versions

hal-01728931 , version 1 (12-03-2018)

Identifiants

Citer

F.E. Spetale, P. Bulacio, S. Guillaume, J. Murillo, E. Tapia. A spectral envelope approach towards effective SVM-RFE on infrared data. Pattern Recognition Letters, 2016, 71, pp.59-65. ⟨10.1016/j.patrec.2015.12.007⟩. ⟨hal-01728931⟩
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