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 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 …

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

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 …

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 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 …

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

The importance of medical data and the crucial nature of the decisions that are based on
such data, as well as the large increase in its volume, has encouraged researchers to …

In large datasets, irrelevant, redundant, and noisy attributes are often present. These
attributes can have a negative impact on the classification model accuracy. Therefore …