Motivation Feature selection is a powerful dimension reduction technique which selects a
subset of relevant features for model construction. Numerous feature selection methods …

Biological data, including gene expression data, are generally high-dimensional and require
efficient, generalizable, and scalable machine-learning methods to discover complex …

High dimensionality, also known as the curse of dimensionality, is still a major challenge for
automatic classification solutions. Accordingly, several feature selection (FS) strategies have …

In classification problems, the purpose of feature selection is to identify a small, highly
discriminative subset of the original feature set. In many applications, the dataset may have …

Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent
space with minimized defined optimization objectives. The two independent branches of DR …

Feature selection, as a vital dimension reduction technique, reduces data dimension by
identifying an essential subset of input features, which can facilitate interpretable insights …

High-dimensional data in many machine learning applications leads to computational and
analytical complexities. Feature selection provides an effective way for solving these …

A common problem in machine learning and pattern recognition is the process of identifying
the most relevant features, specifically in dealing with high-dimensional datasets in …

Learning from large dimensional data presents major challenges related to the size of the
data. Thus, dimensionality reduction techniques such as feature selection are brought in to …

Dimensionality reduction (DR) is an important technique for data exploration and knowledge
discovery. However, most of the main DR methods are either linear (eg, PCA), do not …