Epilepsy is a group of neurological disorders that affect normal brain activities and human
behavior. Electroencephalogram based automatic epileptic seizure detection has significant …

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

Feature selection is a pre-processing technique used to remove unnecessary
characteristics, and speed up the algorithm's work process. A part of the technique is carried …

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 …

In multi-label learning, feature selection is a topical issue for addressing high-dimension
data. However, most of existing methods adopt imperfect labels to perform feature selection …

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 plays a vital role in many fields, particularly in pattern recognition and
bioinformatics, for selecting informative and relevant features from high dimensional …

Abstract Information theory has been widely used to evaluate the relevance and redundancy
of features in feature selection. The traditional feature selection methods based on …

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