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 …

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that
has been applied in almost all areas of optimization, as well as engineering practice. Many …

Classification accuracy highly dependents on the nature of the features in a dataset which
may contain irrelevant or redundant data. The main aim of feature selection is to eliminate …

Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic
algorithms. In this paper, two hybridization models are used to design different feature …

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 …

Searching for the optimal subset of features is known as a challenging problem in feature
selection process. To deal with the difficulties involved in this problem, a robust and reliable …

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 …

We propose a new feature selection strategy based on rough sets and particle swarm
optimization (PSO). Rough sets have been used as a feature selection method with much …

Semantics-preserving dimensionality reduction refers to the problem of selecting those input
features that are most predictive of a given outcome; a problem encountered in many areas …

Feature selection (FS) can be defined as the problem of finding the minimal number of
features from an original set with the minimum information loss. Since FS problems are …