Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …

The approximation of low-order information-theoretic terms for feature selection approaches
has achieved success in addressing high-dimensional multi-label data. However, three …

Data mining and machine learning algorithms' performance is degraded by data of high-
dimensional nature due to an issue called “curse of dimensionality”. Feature selection is a …

Neighborhood rough sets are now widely used to process numerical data. Nevertheless,
most of the existing neighborhood rough sets are not able to distinguish class mixture …

Data classification is the most common task in machine learning, and feature selection is the
key step in the classification task. Common feature selection methods mainly analyze the …

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) is a significant data preprocessing technique, whose purpose is to
identify feature subset that can improve the prediction accuracy from the subsequent training …

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 is an important preprocessing operation in the fields of machine learning
and data mining. Information theory is widely used in feature selection methods because it …

Aiming at the problems of Flow Direction Algorithm (FDA), such as premature convergence
and tendency to fall into local optimum, this paper proposes an efficient improved version …