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 …

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 …

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 …

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

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 …

Feature selection (FS) plays a crucial role as a pre-processing tool in data mining, especially
for real-world applications in medical fields; it has been utilized exponentially and becomes …

This paper proposed a Multi-Criteria Decision-Making (MCDM) modeling to deal with multi-
target regression problem. This model offered a feature ranking approach for multi-target …