Learning algorithms can be less effective on datasets with an extensive feature space due to
the presence of irrelevant and redundant features. Feature selection is a technique that …

Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

The increasing trend toward using the Internet of Things (IoT) increased the number of
intrusions and intruders annually. Hence, the integration, confidentiality, and access to …

The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …

This systematic review provides researchers interested in feature selection (FS) for
processing microarray data with comprehensive information about the main research …

Feature selection, an optimization problem, becomes an important pre-process tool in data
mining, which simultaneously aims at minimizing feature-size and maximizing model …

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play
a crucial role in algorithms performance. It reduces the processing time and accuracy of the …

This paper introduces a comprehensive survey of a new swarm intelligence optimization
algorithm so-called Harris hawks optimization (HHO) and analyzes its major features. HHO …

The Arithmetic Optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm
that has been shown to perform well in several benchmark tests. The AOA is a metaheuristic …

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 …