Feature selection is a preprocessing technique that identifies the key features of a given
problem. It has traditionally been applied in a wide range of problems that include biological …

In an era of growing data complexity and volume and the advent of big data, feature
selection has a key role to play in hel** reduce high-dimensionality in machine learning …

Feature selection (FS) is an important data processing technique in the field of machine
learning. There have been various FS methods, but all assume that the cost associated with …

This paper offers a comprehensive approach to feature selection in the scope of
classification problems, explaining the foundations, real application problems and the …

Since different features may require different costs, the cost-sensitive feature selection
problem become more and more important in real-world applications. Generally, it includes …

Feature selection is an important data-preprocessing technique in classification problems
such as bioinformatics and signal processing. Generally, there are some situations where a …

Feature selection plays an important role in the machine-vision-based online detection of
foreign fibers in cotton because of improvement detection accuracy and speed. Feature sets …

This article addresses the explainable machine learning for data extraction on diverse
datasets. In many cases, individual or specific approaches have been developed for feature …

The information needed for a certain machine application can be often obtained from a
subset of the available features. Strongly relevant features should be retained to achieve …

Feature selection is an optional preprocessing procedure and is frequently used to improve
the classification accuracy of a machine learning algorithm by removing irrelevant and/or …