Fast Selection of Spectral Variables with B-Spline Compression
| العنوان: | Fast Selection of Spectral Variables with B-Spline Compression |
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| المؤلفون: | Fabrice Rossi, Damien François, Marc Meurens, Michel Verleysen, Vincent Wertz |
| المساهمون: | Usage-centered design, analysis and improvement of information systems (AxIS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria), Centre for Systems Engineering and Applied Mechanics (CSAM), Université Catholique de Louvain = Catholic University of Louvain (UCL), Unité de Biochimie de la Nutrition (BNUT), Dispositifs Intégrés et Circuits Electroniques Machine Learning Group (DICE - MLG) |
| المصدر: | Chemometrics and Intelligent Laboratory Systems Chemometrics and Intelligent Laboratory Systems, 2007, 86 (2), pp.208-218. ⟨10.1016/j.chemolab.2006.06.007⟩ Chemometrics and Intelligent Laboratory Systems, Elsevier, 2007, 86 (2), pp.208-218. ⟨10.1016/j.chemolab.2006.06.007⟩ |
| بيانات النشر: | HAL CCSD, 2007. |
| سنة النشر: | 2007 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, media_common.quotation_subject, Feature selection, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Statistics - Applications, 01 natural sciences, nonlinear method, Machine Learning (cs.LG), Analytical Chemistry, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics, 0202 electrical engineering, electronic engineering, information engineering, Applications (stat.AP), mutual information, Spectroscopy, Interpretability, media_common, Mathematics, Variables, Process Chemistry and Technology, B-spline, 010401 analytical chemistry, incremental feature selection, Nonparametric statistics, Mutual information, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], neural networks, 0104 chemical sciences, Computer Science Applications, Computer Science - Learning, Spline (mathematics), Nonlinear system, 020201 artificial intelligence & image processing, B-spline compression, Algorithm, Software, [CHIM.CHEM]Chemical Sciences/Cheminformatics, variable selection |
| الوصف: | International audience; The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables hopefully can be reduced, by using either projection techniques or selection methods; the latter allow for the interpretation of the selected variables. Since the optimal approach of testing all possible subsets of variables with the prediction model is intractable, an incremental selection approach using a nonparametric statistics is a good option, as it avoids the computationally intensive use of the model itself. It has two drawbacks however: the number of groups of variables to test is still huge, and colinearities can make the results unstable. To overcome these limitations, this paper presents a method to select groups of spectral variables. It consists in a forward-backward procedure applied to the coefficients of a B-Spline representation of the spectra. The criterion used in the forward-backward procedure is the mutual information, allowing to find nonlinear dependencies between variables, on the contrary of the generally used correlation. The spline representation is used to get interpretability of the results, as groups of consecutive spectral variables will be selected. The experiments conducted on NIR spectra from fescue grass and diesel fuels show that the method provides clearly identified groups of selected variables, making interpretation easy, while keeping a low computational load. The prediction performances obtained using the selected coefficients are higher than those obtained by the same method applied directly to the original variables and similar to those obtained using traditional models, although using significantly less spectral variables. |
| اللغة: | English |
| تدمد: | 0169-7439 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2203d726068af1c2633eba3ce6057841 https://inria.hal.science/inria-00174299/file/bsplines-im.pdf |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....2203d726068af1c2633eba3ce6057841 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 01697439 |
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