Title: Random forest automated supervised classification of Hipparcos periodic variable stars
Authors: Dubath, P ×
Rimoldini, L
Sueveges, M
Blomme, Jonas
Lopez, M
Sarro, L. M
De Ridder, Joris
Cuypers, J
Guy, L
Lecoeur, I
Nienartowicz, K
Beck, M
Mowlavi, N
De Cat, P
Lebzelter, T
Eyer, L #
Issue Date: Jul-2011
Publisher: Priestley and Weale
Series Title: Monthly Notices of the Royal Astronomical Society vol:414 issue:3 pages:2602-2617
Abstract: We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train supervised algorithms to characterize the type dependencies on a number of attributes. The most useful attributes evaluated with the random forest methodology include, in decreasing order of importance, the period, the amplitude, the V - I colour index, the absolute magnitude, the residual around the folded light-curve model, the magnitude distribution skewness and the amplitude of the second harmonic of the Fourier series model relative to that of the fundamental frequency. Random forests and a multi-stage scheme involving Bayesian network and Gaussian mixture methods lead to statistically equivalent results. In standard 10-fold cross-validation (CV) experiments, the rate of correct classification is between 90 and 100 per cent, depending on the variability type. The main mis-classification cases, up to a rate of about 10 per cent, arise due to confusion between SPB and ACV blue variables and between eclipsing binaries, ellipsoidal variables and other variability types. Our training set and the predicted types for the other Hipparcos periodic stars are available online.
ISSN: 0035-8711
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Institute of Astronomy
× corresponding author
# (joint) last author

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