  Upcoming synoptic surveys are set to generate an unprecedented amount of
data. This requires an automatic framework that can quickly and efficiently
provide classification labels for several new object classification challenges.
Using data describing 11 types of variable stars from the Catalina Real-Time
Transient Surveys (CRTS), we illustrate how to capture the most important
information from computed features and describe detailed methods of how to
robustly use Information Theory for feature selection and evaluation. We apply
three Machine Learning (ML) algorithms and demonstrate how to optimize these
classifiers via cross-validation techniques. For the CRTS dataset, we find that
the Random Forest (RF) classifier performs best in terms of balanced-accuracy
and geometric means. We demonstrate substantially improved classification
results by converting the multi-class problem into a binary classification
task, achieving a balanced-accuracy rate of $\sim$99 per cent for the
classification of ${\delta}$-Scuti and Anomalous Cepheids (ACEP). Additionally,
we describe how classification performance can be improved via converting a
'flat-multi-class' problem into a hierarchical taxonomy. We develop a new
hierarchical structure and propose a new set of classification features,
enabling the accurate identification of subtypes of cepheids, RR Lyrae and
eclipsing binary stars in CRTS data.
