  Discovering pulsars is a significant and meaningful research topic in the
field of radio astronomy. With the advent of astronomical instruments such as
he Five-hundred-meter Aperture Spherical Telescope (FAST) in China, data
volumes and data rates are exponentially growing. This fact necessitates a
focus on artificial intelligence (AI) technologies that can perform the
automatic pulsar candidate identification to mine large astronomical data sets.
Automatic pulsar candidate identification can be considered as a task of
determining potential candidates for further investigation and eliminating
noises of radio frequency interferences or other non-pulsar signals. It is very
hard to raise the performance of DCNN-based pulsar identification because the
limited training samples restrict network structure to be designed deep enough
for learning good features as well as the crucial class imbalance problem due
to very limited number of real pulsar samples. To address these problems, we
proposed a framework which combines deep convolution generative adversarial
network (DCGAN) with support vector machine (SVM) to deal with imbalance class
problem and to improve pulsar identification accuracy. DCGAN is used as sample
generation and feature learning model, and SVM is adopted as the classifier for
predicting candidate's labels in the inference stage. The proposed framework is
a novel technique which not only can solve imbalance class problem but also can
learn discriminative feature representations of pulsar candidates instead of
computing hand-crafted features in preprocessing steps too, which makes it more
accurate for automatic pulsar candidate selection. Experiments on two pulsar
datasets verify the effectiveness and efficiency of our proposed method.
