Fuzzy information-theoretic metrics have been demonstrated to be effective in evaluating
feature relevance and redundancy in both categorical and numerical feature selection tasks …

Reservoir water sensitivity damage significantly contributes to production declines in low-
permeability oil and gas fields. An accurate and rapid assessment of water sensitivity is …

The neighborhood information theory have been used to evaluate the relevance and
redundancy in feature selection for mixed data containing discrete and continuous features …

Feature selection is a crucial preprocessing technique in data mining and machine learning
and has attracted increasing attentions. However, the relevance of existing methods only …

Feature selection (FS) based on mutual information (MI) metrics needs to discretize the data
in preprocessing, which is a convenient way to identify correlation between features …

Feature selection plays a significant role in data mining and machine learning. It is
challenging to determine how many features are necessary to form an optimal feature …

Locating low-velocity impacts (LVIs) on composite plates precisely is necessary. Support
vector regression (SVR) is an effective method in addressing the LVI localization problem …

Feature selection aims to select crucial features to improve classification accuracy in
machine learning and data mining. Existing methods concentrate on the classification …

Feature selection is an important dimensionality reduction technique in machine learning,
pattern recognition, image processing, and data mining. Most existing feature selection …

Feature selection (FS) is a significant dimensionality reduction technique in machine
learning and data mining that is adept at managing high-dimensional data efficiently and …