Journal ArticleDOI
Application of genetic algorithm–PLS for feature selection in spectral data sets
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TLDR
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 because the variables selected by the algorithm often correspond to well‐defined and characteristic spectral regions instead of being single variables scattered throughout the spectrum.Abstract:
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.read more
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BookDOI
Handbook of Water Analysis
TL;DR: In this paper, the authors present a sampling and data treatment method for water analysis, which is based on the analysis of water samples collected by the National Institute of Water and Environmental Sciences.
Journal ArticleDOI
Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions
Riccardo Leardi,Lars Nørgaard +1 more
TL;DR: A modification of interval partial least squares (iPLS), designated backward interval PLS (biPLS) is developed and studied such that it can detect and remove the least relevant regions, thereby reducing the search domain to a size that GAs can handle easily as mentioned in this paper.
Journal ArticleDOI
An overview of variable selection methods in multivariate analysis of near-infrared spectra
TL;DR: This paper generalizes variable selection methods in a simple manner to introduce their classifications, merits and drawbacks, to provide a better understanding of their characteristics, similarities and differences.
Journal ArticleDOI
Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature
TL;DR: Fresh-leaf reflectance spectroscopy and a partial least-squares regression analysis were used to estimate key determinants of photosynthetic capacity—namely the maximum rates of RuBP carboxylation (Vcmax) and regeneration (Jmax)—measured with standard gas exchange techniques on leaves of trembling aspen and eastern cottonwood trees.
Journal ArticleDOI
Validation of chemometric models - a tutorial.
Frank Westad,Federico Marini +1 more
TL;DR: This tutorial focuses on validation both from a numerical and conceptual point of view, and examples of how one data set can be validated across this background information illustrate that it will affect the figures of merits as well as the dimensionality of the models.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Partial least-squares regression: a tutorial
Paul Geladi,Bruce R. Kowalski +1 more
TL;DR: In this paper, a tutorial on the Partial Least Squares (PLS) regression method is provided, and an algorithm for a predictive PLS and some practical hints for its use are given.
Journal ArticleDOI
Elimination of Uninformative Variables for Multivariate Calibration
TL;DR: It is concluded that the elimination of uninformative variables can improve predictive ability and be evaluated on simulated data.
Journal ArticleDOI
Genetic algorithms as a strategy for feature selection
TL;DR: The subsets of variables selected by genetic algorithms are generally more efficient than those obtained by classical methods of feature selection, since they can produce a better result by using a lower number of features.