  It is an active topic to investigate the schemes based on machine learning
(ML) methods for detecting pulsars as the data volume growing exponentially in
modern surveys. To improve the detection performance, input features into an ML
model should be investigated specifically. In the existing pulsar detection
researches based on ML methods, there are mainly two kinds of feature designs:
the empirical features and statistical features. Due to the combinational
effects from multiple features, however, there exist some redundancies and even
irrelevant components in the available features, which can reduce the accuracy
of a pulsar detection model. Therefore, it is essential to select a subset of
relevant features from a set of available candidate features and known as
{\itshape feature selection.} In this work, two feature selection algorithms
----\textit{Grid Search} (GS) and \textit{Recursive Feature Elimination}
(RFE)---- are proposed to improve the detection performance by removing the
redundant and irrelevant features. The algorithms were evaluated on the
Southern High Time Resolution University survey (HTRU-S) with five pulsar
detection models. The experimental results verify the effectiveness and
efficiency of our proposed feature selection algorithms. By the GS, a model
with only two features reach a recall rate as high as 99\% and a false positive
rate (FPR) as low as 0.65\%; By the RFE, another model with only three features
achieves a recall rate 99\% and an FPR of 0.16\% in pulsar candidates
classification. Furthermore, this work investigated the number of features
required as well as the misclassified pulsars by our models.
