Mathematical Approach
Naren Kathirvel, Kathirvel Ayyaswamy, N. Saravanan, P. Kaliappan · Advances in electronic government, digital divide, and regional development book series · 2024
Search engine to identify the promising feature subset candidates and a criterion to identify the best candidate often comprise a feature selection framework. A crucial technique in the field of machine learning, feature selection is given new significance in information retrieval (IR) 5.0. This strategy, sometimes referred to as variable or attribute selection, selects a subset of important traits or predictors in order to construct a model. Selected as a crucial technique for dimensionality reduction, its main goal from the viewpoint of IR 5.0 is to carefully select a subset of features from the original set. Part of the curation process is eliminating anything that is noisy, superfluous, or redundant. In the IR 5.0 era, feature selection becomes a central role as information retrieval is marked by unprecedented sophistication. Feature set accelerates both the improvement of learning performance and increased accuracy in the learning process.