Application of genetic algorithm–PLS for feature selection in spectral data sets

By R. Leardi. 2000

549 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ff6d8b22ecbc80ab8c12a6f0aadb4c436d67c1aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. A. Gomes
2. Edvan Cirino da Silva
3. Germano Véras
4. Roberto Kawakami Harrop Galvão
5. Mário César Ugulino de Araújo
6. P. Lin
7. Yongming Chen
8. Yong He

In Control and Systems Engineering

1. P. Lin
2. Roberto Kawakami Harrop Galvão
3. Mário César Ugulino de Araújo
4. Edvan Cirino da Silva
5. A. Gomes
6. Yongming Chen
7. Germano Véras
8. Yong He

Citations in the corpus

• The successive projections algorithm for interval selection in PLS (2013, methodology): “This investigation is in line with previous works that have reported improvements in PLS through the use of variable selection strategies, such as Genetic Algorithms (GA-PLS) [25], Backward Variable Selection for PLS [26], Iterative Predictor Weighting PLS [27], Elimination of Uninformative Variable”
• Identification of Geographical Origin of Olive Oil Using Visible and Near-Infrared Spectroscopy Technique Combined with Chemometrics (2009, methodology): “Among those variable selection methods, GAs applied to PLS have been shown very efficient and provided better results in many applications than the full spectrum approach (Leardi 2000).”