Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms |
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| Authors: | J Federico Ramírez Olac Fuentes Ravi K Gulati |
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| Institution: | (1) Instituto Nacional de Astrofísica, óptica y Electrónica, Luis Enrique Erro # 1, Santa María Tonanzintla, Puebla, 72840, México;(2) Instituto Nacional de Astrofísica, óptica y Electrónica, Luis Enrique Erro # 1, Santa María Tonanzintla, Puebla, 72840, México |
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| Abstract: | In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices.
This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts
their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature,
surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results
show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive
accuracy error up to 35%.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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| Keywords: | prediction genetic algorithms machine learning optimization |
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