Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …

Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

The “curse of dimensionality” and the high computational cost have still limited the
application of the evolutionary algorithm in high-dimensional feature selection (FS) …

One of the major problems in Big Data is a large number of features or dimensions, which
causes the issue of “the curse of dimensionality” when applying machine learning …

Feature selection (FS) is an important data processing technique in the field of machine
learning. There have been various FS methods, but all assume that the cost associated with …

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 …

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

Feature selection has gained much consideration from scholars working in the domain of
machine learning and data mining in recent years. Feature selection is a popular problem in …

Feature selection is a multi-objective optimization problem since it has two conflicting
objectives: maximizing the classification accuracy and minimizing the number of the …

Feature selection is one of the important techniques of dimensionality reduction in data
preprocessing because datasets generally have redundant and irrelevant features that …