  A significant degree of misclassification of variable stars through the
application of machine learning methods to survey data motivates a search for
more reliable and accurate machine learning procedures, especially in light of
the very large data cubes that will be generated by future surveys and the need
for immediate production of accurate, formalised catalogues of variable
behaviour to enable science to proceed. In this study, the efficiency of an
ensemble machine learning procedure utilising extreme boosting was determined
by application to a large sample of data from the OGLE III and IV surveys and
from the \textit{Kepler} mission. Through recursive training of classifiers,
the study developed a variable star classification workflow which produced an
average efficiency determined with the average precision of the model (0.81 for
\textit{Kepler} and 0.91 for OGLE) and the $f-score$ of predictions on the test
sets. This suggests that extreme boosting can be presented as one of the
favourable shallow learning methods in developing a variable star classifier
for future large survey projects.
