Background: Feature selection sometimes also known as attribute subset selection is a
process in which optimal subset of features are elected with respect to target data by …

Feature selection process is considered as one of the most difficult challenges in machine
learning and has attracted many researchers recently. The main disadvantages of the …

Feature selection is an important data processing method to reduce dimension of the raw
datasets while preserving the information as much as possible. In this paper, an enhanced …

Feature selection is one of the key components of data mining and machine learning
domain that selects the best subset of features with respect to target data by removing …

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 algorithm explores the data to eliminate noisy, irrelevant, redundant data,
and simultaneously optimize the classification performance. In this paper, a classification …

Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in
local optima areas may still be a concern. Several significant GWO factors can be explored …

Feature selection problem is concerned with searching in a dataset for a set of features
aiming to reduce the training time and enhance the accuracy of a classification method …

Feature selection techniques have been presented to allow us to choose a small subset of
the original components' relevant features by removing irrelevant or redundant features …

Feature Subset Selection is an essential pre-processing task in Data Mining. Feature
selection process refers to choosing subset of attributes from the set of original attributes …