  Pulsar search is always the basis of pulsar navigation, gravitational wave
detection and other research topics. Currently, the volume of pulsar candidates
collected by Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows
an explosive growth rate that has brought challenges for its pulsar candidate
filtering System. Particularly, the multi-view heterogeneous data and class
imbalance between true pulsars and non-pulsar candidates have negative effects
on traditional single-modal supervised classification methods. In this study, a
multi-modal and semi-supervised learning based pulsar candidate sifting
algorithm is presented, which adopts a hybrid ensemble clustering scheme of
density-based and partition-based methods combined with a feature-level fusion
strategy for input data and a data partition strategy for parallelization.
Experiments on both HTRU (The High Time Resolution Universe Survey) 2 and FAST
actual observation data demonstrate that the proposed algorithm could
excellently identify the pulsars: On HTRU2, the precision and recall rates of
its parallel mode reach 0.981 and 0.988. On FAST data, those of its parallel
mode reach 0.891 and 0.961, meanwhile, the running time also significantly
decrease with the increment of parallel nodes within limits. So, we can get the
conclusion that our algorithm could be a feasible idea for large scale pulsar
candidate sifting of FAST drift scan observation.
