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 …

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 …

For data mining tasks on high-dimensional data, feature selection is a necessary pre-
processing stage that plays an important role in removing redundant or irrelevant features …

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 …

A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization
(PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO …

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 …

Feature selection is imperative in machine learning and data mining when we have high-
dimensional datasets with redundant, nosy and irrelevant features. The area of feature …

Feature selection or dimensionality reduction can be considered as a multi-objective
minimization problem with two objectives: minimizing the number of features and minimizing …

The last decades have witnessed accumulation in biomedical data. Though they can be
analyzed to enhance assessment of at-risk patients and improve the diagnosis, a major …

Feature selection is an effective approach to reduce the number of features of data, which
enhances the performance of classification in machine learning. In this paper, we formulate …