  The field of Astronomy requires the collection and assimilation of vast
volumes of data. The data handling and processing problem has become severe as
the sheer volume of data produced by scientific instruments each night grows
exponentially. This problem becomes extensive for conventional methods of
processing the data, which was mostly manual, but is the perfect setting for
the use of Machine Learning approaches. While building classifiers for
Astronomy, the cost of losing a rare object like supernovae or quasars to
detection losses is far more severe than having many false positives, given the
rarity and scientific value of these objects. In this paper, a Linear Support
Vector Machine (LSVM) is explored to detect Quasars, which are extremely bright
objects in which a supermassive black hole is surrounded by a luminous
accretion disk. In Astronomy, it is vital to correctly identify quasars, as
they are very rare in nature. Their rarity creates a class-imbalance problem
that needs to be taken into consideration. The class-imbalance problem and high
cost of misclassification are taken into account while designing the
classifier. To achieve this detection, a novel classifier is explored, and its
performance is evaluated. It was observed that LSVM along with Ensemble Bagged
Trees (EBT) achieved a 10x reduction in the False Negative Rate, using the
Learning from Mistakes methodology.
