HIGHLIGHTS
SUMMARY
Feature selection method is a data preprocessing approach that selects feature subspaces from the original feature space that contribute to the target. According to whether the processed dataset has labels, it can be divided into supervised feature selection and unsupervised feature selection. The similarity of variables is described by an unsupervised learning method (K-means), in which a series of target groups are divided, and the feature that maximizes the variable information value (IV) is selected as a representative from each group, thereby forming a new feature subset, and this method is recorded as . . .
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