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

In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Intradialytic hypotension (IDH) is the most common acute complication in hemodialysis (HD)
sessions and is associated with increased morbidity and mortality in HD patients. To prevent …

The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …

Feature selection is an important preprocessing technology for dimensionality reduction,
which reduces the dimension of the dataset by acquiring a subset of features with the largest …

Text classification is a challenging problem due to the high dimensionality of the text, which
can limit classification performance. The orthogonal matching pursuit (OMP) algorithm is one …

Feature selection (FS), which aims to select informative feature subsets and improve
classification performance, is a crucial data-mining technique. Recently, swarm intelligence …

With the extensive applicability of machine learning classification algorithms to a wide
spectrum of domains, feature selection (FS) becomes a relevant data preprocessing …

The high dimensionality of data hinders the learning ability of machine learning algorithms.
Feature selection techniques can be used to reduce dimensionality, which is an important …

Email authorship analysis is a challenging task involving the detection of an author's style to
help determine their identity. Emails represent a widespread application of big data, and …