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
2016
Spetale, F.E. | Bulacio, P. | Guillaume, S. | Murillo, J. | Tapia, E. | Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) ; Université Paul Cézanne - Aix-Marseille 3-Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET)-Universidad Nacional de Rosario [Santa Fe] | Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP) ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE
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显示更多 [+] 显示较少 [-]英语. 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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