  With the availability of large-scale surveys like Kepler and TESS, there is a
pressing need for automated methods to classify light curves according to known
classes of variable stars. We introduce a new algorithm for classifying light
curves that compares 7000 time-series features to find those which most
effectively classify a given set of light curves. We apply our method to Kepler
light curves for stars with effective temperatures in the range 6500--10,000K.
We show that the sample can be meaningfully represented in an interpretable
five-dimensional feature space that separates seven major classes of light
curves (delta Scuti stars, gamma Doradus stars, RR Lyrae stars, rotational
variables, contact eclipsing binaries, detached eclipsing binaries, and
non-variables). We achieve a balanced classification accuracy of 82% on an
independent test set of Kepler stars using a Gaussian mixture model classifier.
We use our method to classify 12,000 Kepler light curves from Quarter 9 and
provide a catalogue of the results. We further outline a confidence heuristic
based on probability density with which to search our catalogue, and extract
candidate lists of correctly-classified variable stars.
