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 (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

Availability of suitable and validated data is a key issue in multiple domains for
implementing machine learning methods. Higher data dimensionality has adverse effects on …

Nowadays, microarray data processing is one of the most important applications in
molecular biology for cancer diagnosis. A major task in microarray data processing is gene …

The Arithmetic Optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm
that has been shown to perform well in several benchmark tests. The AOA is a metaheuristic …

The recent advancements in science, engineering, and technology have facilitated huge
generation of datasets. These huge datasets contain noisy, redundant, and irrelevant …

Heart disease is one of the most complicated diseases, and it affects a large number of
individuals throughout the world. In healthcare, particularly cardiology, early and accurate …

This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic
Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being …

Feature selection (FS) is commonly thought of as a pre-processing strategy for determining
the best subset of characteristics from a given collection of features. Here, a novel discrete …

According to the WHO, Cancer is a prominent cause of mortality worldwide, accounting for~
10 million fatalities at the end of 2020. The most common types of cancers include Lung …