Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …

Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

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 …

Meta-heuristics are problem-independent optimization techniques which provide an optimal
solution by exploring and exploiting the entire search space iteratively. These techniques …

In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and
used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA …

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing
the number of features by removing irrelevant, redundant and noisy data while maintaining …

The feature selection (FS) problem has occupied a great interest of scientists lately since the
highly dimensional datasets might have many redundant and irrelevant features. FS aims to …

Abstract Salp Swarm Algorithm (SSA) is one of the most recently proposed algorithms driven
by the simulation behavior of salps. However, similar to most of the meta-heuristic …