Metaheuristic algorithms are computational intelligence paradigms especially used for
sophisticated solving optimization problems. This chapter aims to review of all …

Featured Application The paper analyzes the usage and mechanisms of feature selection
methods that are based on swarm intelligence in different application areas. Abstract The …

Feature selection (FS) is an important preprocessing technique for dimensionality reduction
in classification problems. Particle swarm optimization (PSO) algorithms have been widely …

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 …

Swarm intelligence is a kind of artificial intelligence that is based on the collective behavior
of the decentralized and self-organized systems. This work focuses on reviewing a heuristic …

A tremendous flow of big data has come from the growing use of digital technology and
intelligent systems. This has resulted in an increase in not just the dimensional issues that …

In this paper, a feature selection (FS) method is proposed to identify key quality features
(KQFs) in complex manufacturing processes. We propose a multi-objective binary particle …

Particle swarm optimization (PSO) is a heuristic optimization algorithm generally applied to
continuous domains. Binary PSO is a form of PSO applied to binary domains but uses the …

Feature selection is the basic pre-processing task of eliminating irrelevant or redundant
features through investigating complicated interactions among features in a feature set. Due …

Class imbalance is one of the challenging problems for machine learning in many real-world
applications. Cost-sensitive learning has attracted significant attention in recent years to …