Genetic Algorithms as a Tool for Wavelength Selection
2004 (English)In: Proceedings of the 11th Finnish Artificial Intelligence Conference (STeP 2004) in Vantaa (Finland), Volume 3, 2004, Vol. 3, 99-113 p.Conference paper (Refereed)
This work is a careful implementation of a genetic algorithm (GA) for pre-selection of wavelengths, combined with partial least squares regression (PLS) for modelling of near-infrared (NIR) data. We show that NIR spectro- metry can be used for concentration measurements when background noise has not been limited and no chemical properties of the substances are known. We use an alternative brute force approach working for any convergent GA. It works by generating many solutions, preferably by using different GA parameters, and then constructing the final solution by only including variables found in a majority of all solutions. The proposed method is based on three assumptions: existence of data, varying measurement noise and selection of data wavelengths that are more fruitful than noise.
Place, publisher, year, edition, pages
2004. Vol. 3, 99-113 p.
Feature selection, Genetic Algorithms, Near-infrared spectrum, Partial least squares, Variable selection
IdentifiersURN: urn:nbn:se:kth:diva-80738OAI: oai:DiVA.org:kth-80738DiVA: diva2:496836
11th Finnish Artificial Intelligence Conference (STeP 2004)