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 …

Feature Selection (FS) methods alleviate key problems in classification procedures as they
are used to improve classification accuracy, reduce data dimensionality, and remove …

In recent years, numerous meta-heuristic algorithms based on swarm intelligence have
been proposed and widely popularized. Although algorithms are designed by some specific …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

Gene expression data have become increasingly important in machine learning and
computational biology over the past few years. In the field of gene expression analysis …

Feature selection (FS) is a crucial process that aims to remove unnecessary features from
datasets. It plays a role in data mining and machine learning (ML) by reducing the risk …

In recent decades, the improvement of computer technology has increased the growth of
high-dimensional microarray data. Thus, data mining methods for DNA microarray data …

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

A tremendous amount of unstructured data, such as comments, opinions, and other sorts of
data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of …

The credit risk prediction technique is an indispensable financial tool for measuring the
default probability of credit applicants. With the rapid development of machine learning and …