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Feature selection for classification of animal feed ingredients from near infrared microscopy spectra

dc.contributor.authorSánchez del Rivero, José Antonio 
dc.contributor.authorMontañés Roces, Elena 
dc.contributor.authorRoza Delgado, María Begoña de la
dc.contributor.authorSoldado Cabezuelo, Ana Belén 
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorBahamonde Rionda, Antonio
dc.identifier.citationInformation Sciences, 241, p. 58-69 (2013); doi:10.1016/j.ins.2013.03.054
dc.description.abstractThe classification of animal feed ingredients has become a challenging computational task since the food crisis that arose in the European Union after the outbreak of bovine spongiform encephalopathy (BSE). The most interesting alternative to replace visual observation under classical microscopy is based on the use of near infrared reflectance microscopy (NIRM). This technique collects spectral information from a set of microscopic particles of animal feeds. These spectra can be classified using maximum margin classifiers with good results. However, it is difficult to interpret the models in terms of the contribution of features. To gain insight into the interpretability of such classifications, we propose a method that learns accurate classifiers defined on a small set of narrow intervals of wavelengths. The proposed method is a greedy bipartite procedure that may be successfully compared with other state-of-the-art feature selectors and can be scaled up efficiently to deal with other classification tasks of higher dimensionality
dc.description.sponsorshipThe research work by the Artificial Intelligence Center reported here is supported in part under Grant TIN2011-23558 from the Spanish Ministerio de Economı´ a y Competitividad. The SERIDA work was supported by the Spanish Project RTA2010-00128-00-00 from the INIA
dc.format.extentp. 58-69
dc.relation.ispartofInformation Sciences, 241eng
dc.rights© 2013 Elsevier
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.subjectFeature selection
dc.subjectInterval selection
dc.subjectVariable selection
dc.titleFeature selection for classification of animal feed ingredients from near infrared microscopy spectraeng

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