High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

Feature selection is the problem of finding the optimal subset of features for predicting class
labels by removing irrelevant or redundant features. S-shaped Binary Butterfly Optimization …

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

Feature selection is an import preprocessing approach for machine learning and text mining.
It reduces the dimensions of high-dimensional data. A popular approach is based on …

Most current mutual information (MI) based feature selection techniques are greedy in
nature thus are prone to sub-optimal decisions. Potential performance improvements could …

Mutual information (MI) based approaches are a popular paradigm for feature selection.
Most previous methods have made use of low-dimensional MI quantities that are only …

The emergence of``curse of dimensionality” issue as a result of high reduces datasets
deteriorates the capability of learning algorithms, and also requires high memory and …

Feature selection is an important preprocessing and interpretable method in the fields where
big data plays an essential role. In this paper, we first reformulate and analyze some …

As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming
more popular. Existing approaches use either information about global or local structure of …

Feature selection is a preprocessing step in many application areas that are relevant to
expert and intelligent systems, such as data mining and machine learning. Feature selection …