In data mining and machine learning, feature selection is a critical part of the process of
selecting the optimal subset of features based on the target data. There are 2n potential …

Selecting the most relevant subset of features from a dataset is a vital step in data mining
and machine learning. Each feature in a dataset has 2n possible subsets, making it …

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 …

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 (FS) is a major preprocessing stage which aims to improve Machine
Learning (ML) models' performance by choosing salient features, while reducing the …

Feature selection is one of the significant steps in classification tasks. It is a pre-processing
step to select a small subset of significant features that can contribute the most to the …

Feature selection is a task of choosing the best combination of potential features that best
describes the target concept during a classification process. However, selecting such …

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 …

The concept of any method to resolve feature selection issues is to identify a subset of the
original features. However, determining a minimal feature subset is considered an NP-hard …

Grey Wolf Optimizer (GWO) simulates the grey wolves' nature in leadership and hunting
manners. GWO showed a good performance in the literature as a meta-heuristic algorithm …