Learning algorithms can be less effective on datasets with an extensive feature space due to
the presence of irrelevant and redundant features. Feature selection is a technique that …

Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …

Spiking neural networks (SNNs) have garnered increased attention in the field of artificial
general intelligence (AGI) research due to their low power consumption, high computational …

Feature selection is an important preprocessing process in machine learning. It selects the
crucial features by removing irrelevant features or redundant features from the original …

Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies
for data with a survival outcome are rare. We compare 14 filter methods for feature selection …

Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …

In machine learning applications for online product offerings and marketing strategies, there
are often hundreds or thousands of features available to build such models. Feature …

Recently, ransomware attacks have been among the major threats that target a wide range
of Internet and mobile users throughout the world, especially critical cyber physical systems …