  Certifying whether an arbitrary quantum system is entangled or not, is, in
general, an NP-hard problem. Though various necessary and sufficient conditions
have already been explored in this regard for lower dimensional systems, it is
hard to extend them to higher dimensions. Recently, an ensemble bagging and
convex hull approximation (CHA) approach (together, BCHA) was proposed and it
strongly suggests employing a machine learning technique for the
separability-entanglement classification problem. However, BCHA does only
incorporate the balanced dataset for classification tasks which results in
lower average accuracy. In order to solve the data imbalance problem in the
present literature, an exploration of the Boosting technique has been carried
out, and a trade-off between the Boosting and Bagging-based ensemble classifier
is explored for quantum separability problems. For the two-qubit and two-qutrit
quantum systems, the pros and cons of the proposed random under-sampling boost
CHA (RUSBCHA) for the quantum separability problem are compared with the
state-of-the-art CHA and BCHA approaches. As the data is highly unbalanced,
performance measures such as overall accuracy, average accuracy, F-measure, and
G-mean are evaluated for a fair comparison. The outcomes suggest that RUSBCHA
is an alternative to the BCHA approach. Also, for several cases, performance
improvements are observed for RUSBCHA since the data is imbalanced.
