  Variable star analysis and classification is an important task in the
understanding of stellar features and processes. While historically
classifications have been done manually by highly skilled experts, the recent
and rapid expansion in the quantity and quality of data has demanded new
techniques, most notably automatic classification through supervised machine
learning. We present an expansion of existing work on the field by analyzing
variable stars in the {\em Kepler} field using an ensemble approach, combining
multiple characterization and classification techniques to produce improved
classification rates. Classifications for each of the roughly 150,000 stars
observed by {\em Kepler} are produced separating the stars into one of 14
variable star classes.
