By identifying relevant features from the original data, feature selection methods can
maintain or improve the classification accuracy and reduce the dimensionality. Recently …

Metaheuristic algorithms are increasingly utilized to solve complex optimization problems
because they can efficiently explore large solution spaces. The moss growth optimization …

Modern data collection technologies may produce thousands of or even more features in a
single dataset. The high dimensionality of data poses a barrier to determining discriminating …

Computer systems store massive amounts of data with numerous features, leading to the
need to extract the most important features for better classification in a wide variety of …

Multigranularity data analysis has recently become an active research topic in the intelligent
computing and data mining fields. Feature selection via multigranularity data analysis is an …

Evolutionary multi-objective optimisation has shown success in feature selection. However,
existing methods often address these tasks independently, disregarding their potential …

Feature selection (FS) is a significant dimensionality reduction technique in machine
learning and data mining that is adept at managing high-dimensional data efficiently and …

It is a challenging task to select the informative features that can maintain the manifold
structure in the original feature space. Many unsupervised feature selection methods still …

Feature selection (FS) is increasingly important in classification tasks. Although
metaheuristic algorithms have been extensively utilized for FS problems, they share the …

In smart buildings, elevator faults may affect the traveling efficiency and the safety of
passengers. It is important to find a quick and accurate method for fault diagnosis to …