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 …

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 …

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global
optimization and engineering design problems. This improvement is proposed to alleviate …

Differential evolution is an effective and practical approach that is widely applied for solving
global optimization problems. Nevertheless, its effectiveness and scalability are decreased …

In this article, an effective metaheuristic algorithm named multi-trial vector-based differential
evolution (MTDE) is proposed. The MTDE is distinguished by introducing an adaptive …

Advancements in medical technology have created numerous large datasets including
many features. Usually, all captured features are not necessary, and there are redundant …

Abstract Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the
proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in …

Medical technological advancements have led to the creation of various large datasets with
numerous attributes. The presence of redundant and irrelevant features in datasets …

This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for
the Feature Selection (FS) problem. FS is an essential machine learning and data mining …

The rapid expansion of medical data poses numerous challenges for Machine Learning
(ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant …