  The accurate automated classification of variable stars into their respective
sub-types is difficult. Machine learning based solutions often fall foul of the
imbalanced learning problem, which causes poor generalisation performance in
practice, especially on rare variable star sub-types. In previous work, we
attempted to overcome such deficiencies via the development of a hierarchical
machine learning classifier. This 'algorithm-level' approach to tackling
imbalance, yielded promising results on Catalina Real-Time Survey (CRTS) data,
outperforming the binary and multi-class classification schemes previously
applied in this area. In this work, we attempt to further improve hierarchical
classification performance by applying 'data-level' approaches to directly
augment the training data so that they better describe under-represented
classes. We apply and report results for three data augmentation methods in
particular: $\textit{R}$andomly $\textit{A}$ugmented $\textit{S}$ampled
$\textit{L}$ight curves from magnitude $\textit{E}$rror ($\texttt{RASLE}$),
augmenting light curves with Gaussian Process modelling ($\texttt{GpFit}$) and
the Synthetic Minority Over-sampling Technique ($\texttt{SMOTE}$). When
combining the 'algorithm-level' (i.e. the hierarchical scheme) together with
the 'data-level' approach, we further improve variable star classification
accuracy by 1-4$\%$. We found that a higher classification rate is obtained
when using $\texttt{GpFit}$ in the hierarchical model. Further improvement of
the metric scores requires a better standard set of correctly identified
variable stars and, perhaps enhanced features are needed.
