ANALYSIS OF FEATURE SELECTION METHODS AND ALGORITHMS

Abstract

This paper analyzes feature selection methods and algorithms for datasets.
With the increasing volume of data, feature selection becomes crucial for
improving algorithm efficiency, compressing data, and reducing the complexity of
analysis. The paper discusses permissible feature selection approaches and
automatic feature selection algorithms, including filter, wrapper, empirical, and
machine learning methods. Factors to consider when selecting algorithms, such as
dataset characteristics, algorithm purpose, efficiency, accuracy, and interpretability, are also discussed.