  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 experiments, the rate of correct
classification is between 90 and 100%, depending on the variability type. The
main mis-classification cases, up to a rate of about 10%, 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.
