Artificial Intelligence based wrapper for high dimensional feature selection.
Journal:
BMC bioinformatics
Published Date:
Oct 18, 2023
Abstract
BACKGROUND: Feature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper algorithm primarily focuses on shortening the path to find an optimal feature set. However, it underutilizes the capability of feature subset models, which impacts feature selection and its predictive performance.