Classification and outlier identification for the GAIA mission
The GAIA satellite is scheduled for launch in 2010. GAIA will observe spectral data of about 1 billion celestial objects. Part of the preparation of the GAIA mission is the choice of an efficient classification method to classify the observed objects automatically as stars, double stars, quasars or other objects. For this reason, there have been two blind testing experiments on simulated data. In this paper, the blind testing procedure is described as well as the results of a cross-validation experiment to choose a good classifier from a broad class of methods, comprising, e.g., the support vector machine, neural networks, nearest neighbor methods, classification trees and random forests. Because of a lack of information about their nature, no outliers ("other objects"-class) have been simulated. A new strategy to identify outliers based on only "clean" training data independent of the chosen classification method is proposed.
quasars support vector machine star survey