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Application of genetic algorithm–PLS for feature selection in spectral data sets
Author(s) -
Leardi Riccardo
Publication year - 2000
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/1099-128x(200009/12)14:5/6<643::aid-cem621>3.0.co;2-e
Subject(s) - feature selection , pattern recognition (psychology) , chemometrics , computer science , selection (genetic algorithm) , genetic algorithm , artificial intelligence , feature (linguistics) , algorithm , data mining , machine learning , philosophy , linguistics
After suitable modifications, genetic algorithms can be a useful tool in the problem of wavelength selection in the case of a multivariate calibration performed by PLS. Unlike what happens with the majority of feature selection methods applied to spectral data, the variables selected by the algorithm often correspond to well‐defined and characteristic spectral regions instead of being single variables scattered throughout the spectrum. This leads to a model having a better predictive ability than the full‐spectrum model; furthermore, the analysis of the selected regions can be a valuable help in understanding which are the relevant parts of the spectra. After the presentation of the algorithm, several real cases are shown. Copyright © 2000 John Wiley & Sons, Ltd.