This survey is an effort to provide a research repository and a useful reference for
researchers to guide them when planning to develop new Nature-inspired Algorithms …

High-dimensional data is ubiquitous in scientific research and industrial production fields. It
brings a lot of information to people, at the same time, because of its sparse and …

Classification accuracy highly dependents on the nature of the features in a dataset which
may contain irrelevant or redundant data. The main aim of feature selection is to eliminate …

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing
the number of features by removing irrelevant, redundant and noisy data while maintaining …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

The prevention of intrusion is deemed to be a cornerstone of network security. Although
excessive work has been introduced on network intrusion detection in the last decade …

Classification problems often have a large number of features in the data sets, but not all of
them are useful for classification. Irrelevant and redundant features may even reduce the …

In classification, feature selection is an important data pre-processing technique, but it is a
difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is …

Abstract Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in …

Wrapper feature selection methods aim to reduce the number of features from the original
feature set to and improve the classification accuracy simultaneously. In this paper, a …