Feature Selection (FS) reduces the number of features by removing unnecessary,
redundant, and noisy information while keeping a relatively decent classification accuracy …

Feature selection (FS) is a necessary process applied to reduce the high dimensionality of
the dataset. It is utilized to obtain the most relevant information and reduce the …

Maximizing the classification accuracy and minimizing the number of selected features are
the two main incompatible objectives for using feature selection to overcome the curse of …

The feature selection problem is one of the pre-processing mechanisms to find the optimal
subset of features from a dataset. The search space of the problem will exponentially grow …

As the volume of data generated by information systems continues to increase, machine
learning (ML) techniques have become essential for the extraction of meaningful insights …

Feature Selection (FS) aims to ameliorate the classification rate of dataset models by
selecting only a small set of appropriate features from the initial range of features. In …

A tremendous flow of big data has come from the growing use of digital technology and
intelligent systems. This has resulted in an increase in not just the dimensional issues that …

Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

Feature selection (FS) is considered as one of the core concepts in the areas of machine
learning and data mining which immensely impacts the performance of classification model …

Feature selection represents an essential pre-processing step for a wide range of Machine
Learning approaches. Datasets typically contain irrelevant features that may negatively …