  We present an automated classification of stars exhibiting periodic,
non-periodic and irregular light variations. The Hipparcos catalogue of
unsolved variables is employed to complement the training set of periodic
variables of Dubath et al. with irregular and non-periodic representatives,
leading to 3881 sources in total which describe 24 variability types. The
attributes employed to characterize light-curve features are selected according
to their relevance for classification. Classifier models are produced with
random forests and a multistage methodology based on Bayesian networks,
achieving overall misclassification rates under 12 per cent. Both classifiers
are applied to predict variability types for 6051 Hipparcos variables
associated with uncertain or missing types in the literature.
