Artificial neural networks (ANNs) have achieved great success in performing machine
learning tasks, including classification, regression, prediction, image processing, image …

Balancing convergence and diversity is a crucial challenge for particle swarm optimization in
addressing large-scale optimization problems. A main reason is that it is difficult to …

Feature selection is one of the popular techniques used to reduce the number of features by
eliminating noisy, unreliable, and unnecessary data without affecting the classification …

Feature selection is a method used to decrease the number of features by removing
unwanted, noisy and inconsistent data while maintaining classification accuracy. Most …

Metaheuristic search algorithms (MSAs) receive increasing popularity in recent year due to
its excellent capability of solving complex real-world optimization problems without …

Feature selection is a vital technique that enhances the quality of input datasets by reducing
redundancy, noise, and inaccuracies without compromising classifier accuracy. The …

Feature selection is a reliable technique for reducing redundant, noisy, or inaccurate
features in raw input datasets without compromising classifier accuracy. Integrating …