AN ENHANCED FEATURE SELECTION METHOD BASED ON GREY WOLF OPTIMIZER FOR CLASSIFICATION PROBLEMS - QASEM AL-TASHI, QASEM ABDULLAH (2021)
This research emphasizes mainly on classification, in which every instance in the dataset is classified into its target class depending on the information depicted by its features. However, it is hard to select the suitable features from a set of features, because the search space is generally large, wherein a dataset contains a number of features that comprise redundant and unnecessary features, which leads in-turn to less performance on the classification. Feature selection is considered the best way to solve this issue by picking up only the most applicable features for the classification. In fact, feature selection aims to remove redundant and irrelevant features and build the model more efficiently. Feature selection categorized into two major types: wrapper and filter, this thesis focuses only on wrapper approaches.