  Pulsar detection has become an active research topic in radio astronomy
recently. One of the essential procedures for pulsar detection is pulsar
candidate sifting (PCS), a procedure of finding out the potential pulsar
signals in a survey. However, pulsar candidates are always class-imbalanced, as
most candidates are non-pulsars such as RFI and only a tiny part of them are
from real pulsars. Class imbalance has greatly damaged the performance of
machine learning (ML) models, resulting in a heavy cost as some real pulsars
are misjudged. To deal with the problem, techniques of choosing relevant
features to discriminate pulsars from non-pulsars are focused on, which is
known as {\itshape feature selection}. Feature selection is a process of
selecting a subset of the most relevant features from a feature pool. The
distinguishing features between pulsars and non-pulsars can significantly
improve the performance of the classifier even if the data are highly
imbalanced.In this work, an algorithm of feature selection called {\itshape
K-fold Relief-Greedy} algorithm (KFRG) is designed. KFRG is a two-stage
algorithm. In the first stage, it filters out some irrelevant features
according to their K-fold Relief scores, while in the second stage, it removes
the redundant features and selects the most relevant features by a forward
greedy search strategy. Experiments on the dataset of the High Time Resolution
Universe survey verified that ML models based on KFRG are capable for PCS,
correctly separating pulsars from non-pulsars even if the candidates are highly
class-imbalanced.
